Patent ID: 12244441

DETAILED DESCRIPTION

In order to mitigate the problem that the literature lacks a mathematically rigorous and robust method for converting the magnitudes output by a bank of 2kcorrelators into a likelihood of each of the corresponding 2korthogonal communication sequences, and then converting these into the likelihoods of each of the k bits having a value of 0 or 1, the inventors of the present invention have described the following in a first section of the description. A communication unit and method for performing soft-decision demodulation, comprising a receiver, is described. The receiver is arranged to receive a transmitted signal conveying a first set of bits comprising k bits that has been selected from a set of 2kpossible signals. The receiver includes a demodulator comprising a bank of 2kcorrelators and configured to: detect a transmission of each possible transmitted signal, and output 2kmagnitudes of correlator outputs, based on the detected possible transmitted signals, by the bank of 2kcorrelators as a first set of inputs. The receiver also includes a de-mapper circuit coupled to the demodulator and configured to receive the first set of inputs; determine statistics derived from a plurality of aggregated correlator output magnitude distributions of the first set of inputs, wherein the plurality of aggregated correlator output magnitude distributions is fewer than 22k; and calculate therefrom and output a first set of aposteriori soft bits comprising k soft bits. In this manner, a first solution provides a soft demapping approach for OS-only correlator outputs that enables high quality soft-decisions to be obtained in a robust and practical manner, which is not dependent on and sensitive to an excessive number of correlator output magnitude distributions.

In addition, in a second section of the description, a soft-decision approach in demapping of a demodulator's correlator outputs from an orthogonal signalling-phase-shift keying (OS-PSK) modulation scheme into extrinsic soft bits in the form of logarithmic likelihood ratios (LLRs)—also known as bit LLRs or soft bits is described, according to some examples of the invention. Here, a Convert to PSK symbol LLRs circuit and a Symbol-to-symbol LLR circuit may additionally be used, whereby a transmitter chain applies a direct-sequence spread spectrum (DSSS) technique and a modulator circuit converts information bits into OS-PSK modulated data. In this manner, the extrinsic soft bits produced for the PSK-modulated bits are consistent with those produced for the OS-modulated bits, expressing a relatively appropriate level of confidence in the bit values.

Correlator Distribution Investigation and Conversion to Symbol LLRs: OS Scheme

Referring now toFIG.1, a top-level block diagram of an OS-scheme transmission system that has been adapted according to example embodiments of the present invention is illustrated. Example embodiments of the present invention provide a receiver circuit in a receiver102of an Orthogonal Signalling (OS) transmission system100that detects each received OS signal using a bank of correlators and demaps the correlator output values into a set of soft bits103, where the soft bits103are fed into a soft-decision channel decoder104. In some examples, this demapper operates in a receiver circuit for performing soft-decision demodulation in a transmission system100comprising a transmitter105, a channel106and a receiver102, wherein the transmitter signals a set of bits107comprising k bits by transmitting an OS transmission signal108that is selected from a set of 2kpossible signals according to the values of the k bits. The receiver102uses a bank of 2kcorrelators to detect the transmission of each possible signal, and wherein the 2kmagnitudes of the correlator outputs109are provided as a set of inputs to the demapper circuit113The demapper circuit113calculates a set of aposteriori soft signals110based on statistics from data analyser116and parameters111derived from a plurality of aggregated correlator output magnitude distributions.

In some examples, the aggregation operation is not directly in the flow from received correlator outputs to soft-demodulated LLRs. Instead, in some examples including the illustrated example, the aggregation may be performed upon the statistics of the channel, for example in advance during an offline look up table process, or for example in advance using an online channel estimation process. For completeness it is confirmed that only the distribution parameters (e.g., s and sigma) extracted from the aggregation that are used in the direct flow from input to output. In some examples, the demapper circuit113is referred to as a soft demapper, since it generates soft signals. These soft signals may then be converted into soft bits103, according to an example process that is demonstrated in the flowchart ofFIG.35and is described later.

In this way, examples of the present invention provide a mathematically rigorous and robust circuit architecture and method of converting the magnitudes output by a bank of 2kcorrelators into a likelihood of each of the corresponding 2korthogonal communication sequences and then converting these into the likelihoods of each of the k bits having a value of ‘0’ or ‘1’, thereby addressing the problem described in the background section.

In examples of the invention, investigations were carried out on the distributions of a demodulator's correlator outputs (also referred to as ‘cross-correlation values’) in order to identify the possibilities of using the distribution characteristics for the soft demapping of correlator outputs into soft signals that may be used to generate soft bits. Also, as part of soft demapping, a circuit was developed (in Matlab) that converts the magnitudes of the correlator outputs into a set of soft magnitudes in a form of logarithmic-likelihood ratios (LLRs), in one example embodiment, by using a plurality of aggregated correlator output magnitude distributions.

Examples of the invention focus on two parts ofFIG.1, namely: characterising the ‘Modulation-channel-specific library of correlator distributions’ look up table112(also identified in step4405inFIG.35A), and development of the demapper circuit113arranged to convert the correlator outputs to OS symbol LLRs (also identified in step4410inFIG.35A).

A skilled artisan in this field may implement some of the hereafter described circuits in hardware, firmware or software, such as CRC, encoder101, bits-to-symbols circuit, modulator, demodulator, correlator and decoder. Hence, the various ways that these circuits can be implemented will not be described in any more detail than is necessary for a skilled person to replicate the concepts described herein.

Data

In order to model the transmitter and the channel, six datasets of correlator output values of a demodulator were used, obtained with different channel conditions, where the datasets were provided by [5]. These datasets are based on three signal-to-noise ratio (SNR) values of 0 dB, −3 dB and −7.5 dB, and based on two channel models: the additive white Gaussian noise (AWGN) channel and the multipath (MP) channel. The data is generated based on an M-ary orthogonal signalling (OS) transmission scheme for M=16 (as shown inFIG.1), where M is a power of 2. This means that every k=log2(M)=4 bits is converted in bits to symbol circuit114into an OS symbol in the transmitter105. The datasets are represented in this document using the following notations: AWGN_0 dB, AWGN_−3 dB, AWGN_-7.5 dB, MP_0 dB, MP_−3 dB, MP_-7.5 dB.

Referring for example toFIG.26, an AWGN_0 db dataset3500for an example OS scheme illustrates a first 20 rows out of 8100, according to some example embodiments of the invention. For example,

FIG.26shows the first twenty rows3501of the dataset AWGN_0 db, where each dataset has 8100 rows of data. Each row in a dataset has one symbol (with a value3502in a range [0,M−1] or [0,15]) and corresponding one of M=16 transmitted codes (with indices in a range [0,M−1] or [0,15]) where there is a cross-correlation magnitude3503for each of the 16 codes. Although this example embodiment uses M=16, it is envisaged that the concepts and definitions described herein can be applied to any value of M=2k.

Amongst the M=16 correlator outputs per symbol, the correlator output with the transmitted code index is assumed to be equal to the symbol value as the “correct” correlator output, as shown by the bold numbers inFIG.26. The remaining M−1=15 correlator outputs in the row for that symbol are assumed to be the “incorrect” correlator outputs, as demonstrated by the non-bold correlator values inFIG.26. For example, the 16throw3504inFIG.26has a symbol value ‘5’ (perhaps for a 4-bit message “0101”), for which the transmitted code with the index 5 is the correct correlator, with a magnitude of 49.9. For the same symbol, codes of indices in the range [0, 4]V [6, 15] are the incorrect correlator outputs, with magnitudes in the range [6.0, 13.2]. The analogy of correct and incorrect can be understood in this high-SNR example, where the code with the highest cross-correlation magnitude nearly always has the ‘correct’ index.

Distribution of Correlator Outputs

As shown inFIG.1, the demapper circuit113is arranged to receives the magnitudes of the correlator outputs109as an input, and has knowledge of the parameters111of the aggregated correlator output magnitude distributions (which are shown as an additional input inFIG.1) and converts the correlator outputs to OS symbol LLRs. Hereafter, the term ‘circuit’ is intended to encompass any electronic circuit or component or arrangement of logic gates, modules, function in hardware or firmware, as well as any function implemented as a software operation, noting that various implementations of the concepts described herein may be implemented using either hardware, firmware, software or any combination thereof, for example depending upon the prevailing application, use, etc., as would be understood to those skilled in the art. In order to support the later discussion of the “Convert to OS LLRs” circuit, this section details the correlator output distributions. More specifically, a later section details an aggregation of the correlator output distributions, according to some example embodiments of the present invention. Following this, a later section details the parameterisation of the aggregated correlator output distributions, allowing the aggregated distributions to be described using a small number of parameter values, according to some example embodiments of the present invention. Finally, a later section discusses how the parameter values may be estimated in practical applications, according to some example embodiments of the present invention.

Number of Distributions

The maximum number of distributions that can be used to calculate OS LLRs for k-bit transmissions is 22k, as explained in the following. Given the relatively large number of symbols (resembling transmissions) in each of the six datasets (with 8100 rows), there are several rows for each symbol value, and hence there exists several sets of M=16 correlator outputs per symbol value. For example, there are four rows3505inFIG.26with a symbol value of ‘7’. The way to obtain the maximum number of distributions is to, for every symbol value and from all dataset rows with this symbol value, obtain the distribution of all correlator values with code index ‘0’, obtain the distribution of all correlator values with code index ‘1’, and so on to code index M−1=15. This way, every symbol value, will have M=16 distributions of correlator outputs, where one corresponds to the correct correlator outputs and the other M−1 (or fifteen) correspond to the incorrect correlator outputs. This is shown at200inFIG.2, whereFIG.2illustrates a) maximum distribution201, and b) two aggregated distribution202, approaches in obtaining distributions of the correlator outputs. As there are M=16 symbol values, there will be an overall of M*M or 22k=256 distributions for one dataset using this approach, as shown inFIG.2. In this figure, the rows in the top (maximum distribution) approach201correspond to different symbol values, and columns show different code indices. Notably, the distributions on the diagonal line203belong to the correct correlator outputs, and the incorrect correlator outputs are characterised by the remaining 22k-2kdistributions204inFIG.2.

It is also possible to aggregate the 22kdistribution into just two distributions for calculating symbol LLRs: (i) the aggregation of the distributions of all the correct correlator output values205, and (ii) the aggregation of the distributions of all the incorrect correlator output values206, as illustrated and referred to as a two aggregated distributions approach202. Here, in this two aggregated distributions approach202, the distributions will not be specific to a single symbol and instead show the characteristics of data corresponding to all symbols. Also, the incorrect distribution will not be specific to a single code index and represents incorrect correlator outputs for all code indices. This contrasts with the maximum distributions approach201, which had a separate distribution for each pair of ‘sent symbol—correlator index’. In the two aggregated distributions approach202, the incorrect206and correct205distributions provide collective representations of their corresponding distributions in the maximum distributions approach201, as shown inFIG.2. The maximum distributions approach201, compared to the two aggregated distributions approach202, therefore provides a more specific representation of the correlator output values. However, the maximum distributions approach201comprises M2=256 distributions, each of which is parameterised by a few parameters, which in some examples of the invention may be considered to introduce excessive complexity. A further concern in some examples may be that the maximum distributions approach may be over tuned and may not be robust to varying channel conditions. Note that an assumption of the two aggregated distributions approach202is that the correct distributions of the M=16 symbols are all similar to each other and that the remaining incorrect distributions are all similar to each other. In experiments the inventors of the present invention found that this assumption is sufficiently valid to enable good operation.

In summary, in some examples, a two aggregated distributions approach202is proposed, wherein the plurality of aggregated correlator output magnitude distributions is two, and wherein the first aggregated correlator output magnitude distribution approximates the aggregation205of the 2kdistributions on the diagonal line203of the output magnitudes of the 2kcorrelators when the corresponding one of the 2kpossible signals was selected as the transmission signal, and wherein the second aggregated correlator output magnitude distribution approximates the aggregation206of the 22k-2kdistributions204of the output magnitudes of the 2kcorrelators when the corresponding one of the 2kpossible signals was not selected as the transmission signal.

Fitting Distributions to Data

In some examples of the invention, in order to fit distributions to the correlator outputs from the six datasets of correlator output values of a demodulator used to model the transmitter and the channel, each dataset is divided into two sets of values: the set of all correct correlator outputs and the set of all incorrect correlator outputs, according to the two aggregated distributions approach described above. Through investigations, to each of the two correlator sets of data, and for all datasets, several distributions (such as normal, Rayleigh, Rician, etc) have been fit using Matlab's fitdist command. The complete list of distributions considered can be found at: https://www.mathworks.com/help/stats/fitdist.html#btu538h-distname. Based on visual observation it was concluded that normal and Rayleigh distributions are good fits for the correct and incorrect sets of correlator data, respectively. In the example investigations, since the normal and the Rayleigh distributions are both special cases of the Rician distribution, it was also concluded that the Rician distribution fits both correct and incorrect data. All the other distributions accepted by the fitdist command (a total of 24 accepted distributions) did not fit well to the data, unless they were also generalisations of the Rician distribution. More specifically, it was found that the Nakagami and Weibull distributions were also good fits for the incorrect data, but they do not also generalise the normal distribution and so they do not offer good fits for the correct data.

Given that the Rician distribution is a generic case of both normal and Rayleigh distributions, the Rician distribution was chosen in some examples of the invention so that comparisons can be made between correct and incorrect sets of correlator outputs. The Rician distribution has two parameters, the non-centrality parameter(s) and the scale parameter (σ), with similar analogies to the mean and variance parameters of normal distributions. Therefore, for each dataset there will be four distribution parameters of the correlator outputs; scorrand σcorrof the correct correlator outputs distribution, and sincorrand σincorrof the incorrect correlator outputs distribution. Note that Rician distributions with certain conditions are special cases of some distributions in the chi family of distributions. For example, assuming a random variable R is Rician distributed with a non-centrality parameters and a scale parameter σ=1, R also follows a noncentral chi distribution with two degrees of freedom, and R2will have a non-central chi-squared distribution with two degrees of freedom and a non-centrality parameter s2. Therefore, a set of correlator output values with a Rician distribution of σ=1 can be modelled using the mentioned chi and chi-squared distributions.

Referring now toFIG.3andFIG.4,FIG.3illustrates a multipath (MP) channel dataset of MP −7.5 dB with correct correlated data for the OS scheme ofFIG.1, using a histogram & Rician distribution fit andFIG.4illustrates an MP −7.5 dB dataset incorrect correlated data for the OS scheme ofFIG.1, using a histogram & Rician distribution fit. In order to demonstrate the distribution of correlator output data graphically, two graphs are plotted for each dataset, correct data300is illustrated inFIG.3and incorrect data400is illustrated inFIG.4for the MP_-7.5 dB dataset of the OS-scheme transmission system ofFIG.1. Each graph has a histogram of the data301and a Rician distribution fitted302to the histogram. The number of histogram bins were chosen automatically by the Matlab function histogram, and the function histfit was used to plot both the histogram and the distribution.

Referring now toFIG.5A,FIG.5B,FIG.5CandFIG.6A,FIG.6B,FIG.6C,FIG.5A-FIG.5Cillustrates the distributions of OS-scheme ofFIG.1correlator outputs for an additive white gaussian noise (AWGN) channel of the datasets ofFIG.3andFIG.4, andFIG.6A-FIG.6Cillustrates the distributions of OS-scheme ofFIG.1correlator outputs for a Multipath communications channel for the datasets ofFIG.3andFIG.4. The distributions for all datasets have been included inFIG.5A,FIG.5B,FIG.5CandFIG.6A,FIG.6B,FIG.6C, which illustrate magnitude and phase distributions of the 16OS-scheme correlator outputs for the AWGN and Multipath channels. Note that the bins are narrower for the incorrect data because the incorrect data sub-sets contain M−1=15 times more samples than the correct data sub-sets.

There is an overlap of correlator values between the correct and incorrect distributions for the MP_-7.5 dB dataset, as shown inFIG.3andFIG.4. Values of the correct distribution starts from around ‘15’, which happens to be far in the middle of the incorrect distribution—near where the mode lies. Also, the incorrect distribution ends in values at around ‘55’, that is in the middle—and close to its mode—of the correct distribution. Overlaps between the correct and incorrect distributions means it is likely that the correct correlator output value is close to, and may be smaller than, the incorrect correlator output values in the same received set of M=16 codes.

By looking at the distributions for other SNRs (illustrated inFIG.5A,FIG.5B,FIG.5CandFIG.6A,FIG.6B,FIG.6C), it can be seen that not all SNRs exhibit overlaps between correct and incorrect distributions. Since the incorrect correlators have always their minimum values near zero, the incorrect distributions have bins starting from values close to zero, regardless of what the SNR is. However, as the SNR goes down from 0 dB, the maximum output values of the incorrect correlators in the distribution increase. Hence, smaller SNRs will have a greater range of incorrect correlator output values. Also, in the correct correlator distributions as the SNR goes down the minimum values decrease, and the maximum values increase. In other words, smaller SNRs will have a greater range of correct correlator values, too, but from both small and large sides of the distribution. Therefore, the smaller the SNR the more chance there will be overlap between values of the correct and incorrect correlator outputs. This confirms the concept that with smaller SNRs, choosing the correct code index becomes more difficult.

Thus, in some examples of the invention, a representation of the two aggregated correlator distributions method202is proposed, wherein the first and second aggregated correlator output magnitude distributions are each represented by a set of distribution parameters. For example, in some instances, the set of distribution parameters may comprise a non-centrality parameter s and a scale parameter a303ofFIG.3.

Estimation Methods

The analysis of the later-described OS-PSK Soft Demapping and EXIT Chart implementation assumes a testbench environment, in which prior knowledge of the true values of the transmitted bits107and OS symbols115ofFIG.1is available. In practical known receivers, this knowledge will not be available and the distribution parameters s and σ111must be estimated in its absence. In order to obtain the distribution parameters111of correlator output values, online or offline approaches (as illustrated at4403inFIG.35A) can be taken. In an online approach, a receiver data analyser circuit116is responsible for analysing the correlator outputs from the demodulator118received at run-time, finding the best distribution to fit to the data, and calculating parameters of the distributions, with the benefits from real-time tuning of distribution parameters.

Offline Approaches

In an offline approach, such as shown inFIG.1and inFIG.35A, the correlator distributions may be calculated offline and used to build a library of correlator distributions on all applicable modulation-channel combinations. For example, the six datasets mentioned earlier in this document can be used as models for different channel conditions and modulation schemes. A configuration-time switch117can be configured to choose the right set of data based on the system's modulation scheme and channel type. In some examples, this could be performed as an extension of any other channel estimation tasks performed by the demodulator118. For example, the outputs of these channel estimation tasks could be used to index a look-up table of pre-computed correlator output distributions. More specifically, an offline estimation of correlator distributions may be used to record correlator outputs for several channel conditions and used to build a library, say in a form of a look-up table (LUT)112, of those distributions. Then, during actual data transmissions, an offline channel estimation method4405identifies the current state of the channel and chooses the corresponding distribution from the look-up table112.

Another offline approach example would be to use a single set of correlator distributions in all cases, irrespective of the varying channel conditions. This single set of correlator distributions could be recorded during a worst-case scenario, perhaps at the lowest SNR, where reliable synchronisation can be achieved. When the channel conditions match this worst-case scenario, the use of the corresponding correlator distributions will ensure the best possible performance. When the channel conditions are better than this worst-case scenario, the chance of decoding success can be expected to increase, even though the assumed correlator distributions are pessimistic compared to the true distributions.

Online Approaches

Referring now toFIG.35A, and as an alternative to using datasets to estimate the distributions offline, online example methods4407and4408are described, which calculate the distribution parameters in the early stages of a real data transmission flow4400, such as when a channel estimation task is in progress. The first online channel estimation method4407is through correlating received synchronisation signals with known synchronisation signals. Let's take an example: suppose that for the transmission of 4-bit messages (k=4) the demodulator118knows that before transmitting data, the transmitter sends a synchronisation sequence of ‘0000’, ‘0001’, . . . , ‘1111’, which has M=16 messages and correspond to symbol values 0 to M−1=15, respectively. Therefore, the demodulator118expects that, amongst the M=16 correlators, the correlator with index ‘0’ should, in the first received transmission, output the highest cross-correlation magnitude. In the same way, correlator with index ‘1’ is expected to output the highest magnitude in the second transmission, and so on until the last symbol is transmitted.

Using the first online channel estimation method4407, as the receiver102is aware of the true symbol values, it can associate every set of M=16 correlator outputs of a transmission to its symbol value. The synchronisation sequence can be transmitted a few times, so that for each symbol value there will be a number of M=16 correlator output sets, (in the same manner as illustrated inFIG.26, but with a different time order), and hence distributions can be estimated based on a required sample size. This online technique is sometimes referred to herein as the correlator distribution estimation using synchronisation sequences of the first online channel estimation method4407. With this first online channel estimation method4407, the maximum distributions approach201ofFIG.2can be used to obtain 22kdistributions, or the two aggregated distributions approach202may be employed to obtain the two correct205and incorrect206aggregated correlator distributions, as shown inFIG.2. It is also generally possible to use a number of distributions between 2 and maximum 22kvalues, which is explained in an example later. Following distribution aggregation, a distribution fitting method (such as the histfit function of Matlab) may be used to estimate the s and σ parameters303ofFIG.3for each aggregated distribution.

FIG.35Aalso identifies a second online technique that can operate concurrent to the data transmissions, referred to as the largest-magnitude correlator distribution estimation of the second online channel estimation method4408. In this second online channel estimation method4408, an assumption is made that the correlator with the largest output magnitude, in normal channel conditions, corresponds to transmitted message value. As any correlator in any transmission is outputting either the largest magnitude or not the largest one, recording the values for each of the two cases for all the received transmissions4402under consideration provides two distributions for any correlator: the distribution when that correlator provides the output with the largest magnitude and the distribution when that correlator provides an output that does not have the largest magnitude. This is shown in the middle columns3601ofFIG.27, which illustrates the correlator output distributions of the largest-magnitude method.

Referring now toFIG.27, a correlator output distribution3600of a largest-magnitude method, according to some example embodiments of the invention. There is a total of M*2=32 distributions of largest and non-largest correlator output magnitudes, shown in the middle columns3601ofFIG.27. As illustrated, the number of distributions here is between 2 and the maximum 22k. As shown, it is also possible to aggregate3603all the largest-magnitude distributions (M=16 of them) and aggregate3604all the non-largest-magnitude distributions (also M=16 of them), each set into a separate distribution producing two distributions, as shown in the right-hand column3602inFIG.27. Again, following distribution aggregation, a distribution fitting method (in some examples such as the histfit function of Matlab) may be used to estimate the s and σ parameters for each aggregated distribution. It is then assumed that the s and σ parameters of the aggregated largest-magnitude distribution provides a reasonable estimate of the corresponding parameters303ofFIG.3of the aggregated correct distribution. Likewise, it may be assumed that the s and σ parameters of the aggregated not-largest-magnitude distribution provides a reasonable estimate of the corresponding parameters303ofFIG.3of the aggregated incorrect distribution.

Note that the largest-magnitude correlator distribution estimation approach in the second online channel estimation method4408ofFIG.35Acould be further refined by completing a first estimation of the s and σ-parameters, then using these to calculate LLRs as detailed in the ‘calculating symbol LLRs’ section below, before they are provided to a channel decoder104inFIG.1. In some examples, the channel decoder104ofFIG.1can then attempt to remove any errors in the LLR sequence and use, say, a Cyclic Redundancy Check (CRC) to determine if it has been successful. If not, then the channel decoder104can provide feedback LLRs to the Data Analyser circuit116ofFIG.1. These LLRs can then be considered and, in some instances, may cause the classification of the correlator outputs between the largest-magnitude and not-largest-magnitude groups to be overridden. More specifically, a correlator output having the largest magnitude for a particular transmission may be swapped for another that does not have the largest magnitude, when forming the largest-magnitude group, if the feedback LLRs provide a sufficiently strong suggestion that the magnitudes do not reflect the correct transmission.

Summary

In summary, three methods to estimate the correlator output magnitude distributions have been described with respect toFIG.35A:the offline channel estimation method4405, of using correlator datasets112, with a) 22k(max.), and b) 2 (min.), number of distributions;the online method of using synchronisation sequences of the first online channel estimation method4407, with a) 22k(max.), and b) 2 (min.), number of distributions; andthe online method of largest-magnitude correlator outputs of the second online channel estimation method4408, with a) 2M, and b) 2, number of distributions

In each of the above three methods, the approach using two distributions comprises of a first aggregated distribution of all the correlator output magnitudes where, each correlator output, in its transmission, is ‘assumed’ to correspond to its transmitted signal, amongst all the M correlator outputs of the same transmission. Likewise, there is a second aggregated distribution of all the correlator output magnitudes where, each correlator output, in its transmission, is ‘assumed’ not to correspond to its transmitted signal. In other words, regardless of which of the above three methods is used, the two aggregated distributions in each method, are assumed to refer to the same two sets of correlator outputs across all three methods.

To explain the above approaches in a different way, the M correlator outputs of any one transmission may be divided into the following two groups. The first group comprises of the single correlator output that corresponds to one of the M possible signals that is selected as the transmission signal. The second group comprises of the M−1 correlator outputs that correspond to the M−1 possible signals, where none is selected as the transmission signal. Assuming several transmissions take place, the magnitudes of the first group of aggregated correlator outputs for several transmissions can be represented by a single distribution which is referred to as the first aggregated correlator output magnitude distribution. In the same way, the second aggregated correlator output magnitude distribution represents the second group of aggregated correlators for several transmissions. Therefore, using any of the three methods of distribution estimation listed above, the approach with two distributions refers to the first and the second aggregated correlator output magnitude distributions.

In investigations carried out by the inventors and as detailed below, the correlator distributions of the offline channel estimation method4405are measured from the available datasets and stored in look up tables112, in order to minimise the effect of estimation error. Furthermore, the two aggregated distribution approach (of the two first and second aggregated distributions) is adopted due to its generality, as compared to other approaches with more distributions, and its robustness to varying channel conditions.

In summary, methods have been described for estimating the parameters of the first and second aggregated correlator output magnitude distributions, wherein one of the following is employed:the first set of distribution parameters of the first aggregated correlator output magnitude distribution are estimated in the first online channel estimation method4407ofFIG.35Aby fitting a first probability distribution to the magnitudes of correlator outputs obtained by correlating received synchronisation signals with known synchronisation signals;the first set of distribution parameters of the second aggregated correlator output magnitude distribution are estimated in the first online channel estimation method4407ofFIG.3Aby fitting a second probability distribution to the magnitudes of correlator outputs obtained by correlating received synchronisation signals with signals other than the known synchronisation signal;the first set of distribution parameters of the first aggregated correlator output magnitude distribution are estimated in the second online channel estimation method4408ofFIG.35Aby fitting a third probability distribution to the magnitudes of correlator outputs having the greatest magnitude among sets of 2kcorrelator outputs obtained by the bank of 2kcorrelators;first set of distribution parameters of the second aggregated correlator output magnitude distribution are estimated in the second online channel estimation method4408ofFIG.3Aby fitting a fourth probability distribution to the magnitudes of the 2k−1 correlator outputs that do not have the greatest magnitude among sets of 2kcorrelator outputs obtained by the bank of 2kcorrelators; orthe first set of distribution parameters of the first and second aggregated correlator output magnitude distributions are selected in the offline channel estimation method4405ofFIG.35Afrom a look-up-table112ofFIG.1.

In examples of the invention, it has also been demonstrated that the first, second, third or fourth probability distributions may be represented using the Rician distribution.

Calculating OS LLRs

Logarithmic Likelihood Ratios (LLRs) are a form of soft decision, which express not only what the most likely value of an uncertain decision is, but also how likely this value is. LLRs can have any real value and are often used to represent likelihood of bits in having values of zero or one. When bits are represented by zeros and ones, they are referred to as ‘hard bits’, and if they are represented using LLRs they are known as ‘soft bits’, and those LLRs are referred to as ‘bit LLRs’. However, it is known that LLRs can also be used to represent analogue signal values or symbols, as opposed to digital bits. In such a case, those LLRs may be referred to as symbol LLRs and the signals represented by the symbol LLRs may be known as ‘soft signals’, as opposed to ‘hard signals’ when the analogue signal values are used without forming into another representation. More specifically, if there are M number of possible values for a symbol, then the associated probabilities may be expressed using a set of M LLRs, where each LLR compares the probability of a particular value having occurred, against the probability that that value did not occur.

The inventors of the present invention developed a Matlab function that takes correlator output magnitudes as the set of inputs to the soft demapper circuit and turns them, using the correlator output distributions, into symbol LLRs (which sometimes may be referred to as soft magnitudes) as the set of apriori soft signals, as shown by Convert to OS LLRs demapper circuit113inFIG.1. Therefore, it can be said that the output magnitudes109of the 2kcorrelators inFIG.1are combined with the s and σ parameters111of the first and second aggregated correlator output magnitude distributions in order to obtain the set of apriori soft signals110comprising 2ksoft magnitudes. Note that if non-Rician distributions were adopted to model the aggregated output magnitude distributions, then the corresponding parameters111may be used in this LLR calculation operation instead. The following discussion uses the case where k=4 and M=16 as an example. However, it is envisaged that the concepts described herein can be extended readily to other values.

InFIG.1, the demapper circuit113arranged to convert the correlator outputs to OS symbol LLRs takes at a time M=16 correlator output magnitudes109(both correct and incorrect) of one received symbol and returns M=16 corresponding symbol LLR values as soft signals110, and performs this for all received4402symbols of a frame. Each of these M=16 symbol LLRs correspond to the same received symbol (i.e., a 4-bit message, k=4) and they collectively demonstrate a probabilistic representation of the 4-bit message. Later in the receiver102, the Convert to Bit LLRs circuit119turns the M=16 symbol LLRs or soft signals110of the original 4-bit message into k=4 bit LLRs, as the set of extrinsic soft bits103, where each bit LLR corresponds to one bit of the 4-bit message. In summary, each transmitted symbol115leads to the generation of a set of M=16 symbol LLRs or soft signals110in the receiver, which are then converted to a set of k=4 bit LLRs, or soft bits103.

Hereafter, the description will first focus on the operation of the demapper circuit113arranged to convert the correlator outputs to OS symbol LLRs, which outputs M=16 OS LLRs or soft signals110per transmitted symbol115. The description will then focus on the operation of Convert to Bit LLRs circuit119ofFIG.1, which outputs k=4 bit LLRs, or soft bits103per transmitted symbol.

The developed Matlab function, which implements the conversion to OS symbol LLRs4410ofFIG.35A, instead of accepting just 16 correlator values, returns symbol LLRs for any number of M correlator values fed to it as a vector, as in step4409ofFIG.35A. In some examples, this function takes the following values as inputs: the vector of the correlator values, the modulation parameter M (M=16 in this work), non-centrality and scale parameters of the two correct and incorrect distributions (scorr, σcorr, sincorr, σincorr). The function also outputs the same number of symbol LLRs as the length of input correlator vector.

Referring now toFIG.7A-FIG.7C, graphs 700 illustrate a conversion to OS symbol LLRs for the AWGN channel for the OS-scheme datasets ofFIG.5A,FIG.5B, andFIG.5C. Concurrently, referring also toFIG.8AandFIG.8B, graphs 800 illustrate a conversion to OS symbol LLRs for a Multipath communications channel for the OS-scheme datasets ofFIG.6A,FIG.6B, andFIG.6C. Graphs 700 illustrate the symbol LLR AWGN values701datasets and graphs 800 illustrate the symbol LLR Multipath values701datasets provided by the developed Matlab function across an entire range of correlator output values; from zero to the maximum (that is the maximum value of the correct correlator outputs). In a similar manner to the correlator output distributions, the range of correlator output values increases as SNR goes down. For example, for the AWGN channel while the minimum correlator output value is zero for all SNRs, the maximum correlator output values increase from near 70 for the 0 dB-SNR dataset to near 100 for the −7.5 dB-SNR dataset. As expected, the functions represented by the symbol LLR plots are all strictly increasing. This is expected as there should not be an LLR value for more than one correlator output value. The range of symbol LLRs, from both positive and negative ends, decrease as SNR goes down. This is expected as the certainty of signals to represent certain values (shown by the correlator outputs) must decrease as the power of noise increases with respect to the signal power, and this is captured by the probabilistic nature of the LLR values.

The definition and derivation of the symbol LLRs is as follows:

symbol⁢LLR=Δln[Pr(correct|corr⁢e⁢lator⁢output)Pr(incorrect|correlator⁢output)][1]

Applying Bayes theorem gives

symbol⁢LLR=ln[Pr(correlator⁢output|correct)×Pr⁡(correct)Pr(correlator⁢output|incorrect)×Pr⁡(incorrect)][2]

Here, the conditional probabilities are characterised by the Rician distribution, resulting in: Pr (correct)=1/M and Pr (incorrect)=(M−1)/M. Making these substitutions gives

symbol⁢LLR=ln[Rician⁢P⁢DF(correlator⁢output,sc⁢o⁢r⁢r⁢ect,σc⁢o⁢r⁢r⁢e⁢c⁢t)RicianP⁢DF(correlator⁢output,si⁢n⁢c⁢o⁢r⁢r⁢ect,σi⁢n⁢c⁢o⁢r⁢r⁢e⁢c⁢t)×(M-1)][3]

In summary, a method for OS demodulation118has been described, wherein the output magnitudes109of the 2kcorrelators ofFIG.1are combined with the first set of distribution parameters111of the first and second aggregated correlator output magnitude distributions in order to obtain a set of apriori soft signals110comprising 2ksoft magnitudes, as in step4410ofFIG.35A.

In accordance with a second aspect of the invention, an example implementation of the Convert to Bit LLRs circuit119ofFIG.1. is described, to explain how the two conversion functions may be evaluated.

Conversion to Bit LLRs and EXIT Chart Evaluation: OS Scheme

Referring now toFIG.9, a top-level block diagram of an alternative OS-scheme transmission system900adapted according to example embodiments of the present invention is illustrated. For testing purposes, the alternative OS-scheme transmission system900was developed in Matlab to perform, in the receiver chain901, soft demapping of the output magnitudes109of a demodulator's118correlators into extrinsic bit LLRs904is performed in the soft demapper circuit907, giving compatibility with the iterative decoding principle. A testbench (again developed in Matlab) evaluates the functionality of the new soft demapping function by measuring the quality of the extrinsic bit LLRs, and presents the quality results in the form of extrinsic information transfer (EXIT) charts.

In this example, the Convert to Bit LLRs circuit906is developed to provide compatibility with an iterative decoding setup alongside the demapper circuit113, arranged to convert the correlator outputs to OS symbol LLRs, in order to build the soft demapper circuit907or soft demapping functionality.

Iterative Decoding Principle

Again, each set of M=16 OS LLRs from a total of N/4×16 OS symbol LLRs (k=4)908generated by the demapper circuit113and corresponds to one 4-bit message. Given that k=4 bit LLRs is sought for each 4-bit message (one bit LLR per message bit), each set of M=16 OS LLRs needs to be converted into a set of k=4 bit LLRs. Therefore, a total of N bit LLRs909may be obtained from N/4×16 OS LLRs from the Convert to Bit LLRs circuit906, as shown inFIG.9. While this conversion by default can be performed without using feedback from the decoder—when the feedback bit LLRs910inFIG.9are removed or hard-wired to provide zero-valued LLRs—the conversion can also benefit (as in step4418ofFIG.35A) from the feedback bit LLRs910, according to the principles of iterative decoding. In this example, M=16 is used, although the inventors recognise and appreciate that the concepts and definitions herein described can be applied to any value of M=2k.

Use (as in step4418ofFIG.35B) of feedback during the conversion (as in step4413ofFIG.35B) to bit LLRs usually happens after the decoder912has failed (as in step4421ofFIG.35B) the cyclic redundancy check (CRC) in the CRC decoder911at the end (as in step4416ofFIG.35B) of its decode process. The decoder912, in such a case, instead of asking for a hybrid automatic repeat request (HARQ) retransmission from the transmitter913, may want to try a further attempt of symbol-bit LLR conversion in the Convert to Bit LLRs circuit906with the hope that the use (as in step4418ofFIG.35B) of feedback bit LLRs910may cause the decode to pass (as in step4422ofFIG.35B) in a subsequent attempt, and hence time, bandwidth and power is saved with respect to a retransmission. The Convert to Bit LLRs circuit906, or simply the Bit LLRs circuit, stores4411the N/4×16 OS LLRs in a memory unit until the iterative decoding process is concluded (as in step4422ofFIG.35B), whereupon it clears the memory. After a second round of OS-symbol-to-bit LLR conversion, if the decoder912fails (as in step4421ofFIG.35B) CRC in the CRC decoder911again, it may still want to try more conversion attempts (as in step4421ofFIG.35B). This is because further iterations of demap-decode uses (as in step4418of FIG. B) a feedback value that is based on a history of past LLRs, and hence it is more likely that the decoder912will pass (as in step4422ofFIG.35B). If the CRC decoder911keeps failing (as in step4421ofFIG.35B), eventually the decoder912may decide to ask for a retransmission (HARQ (as in step4422ofFIG.35B) from the transmitter913, or even abort the current code circuit, instead of another (as in step4421ofFIG.35B) iteration of demap-decode. This process of going through multiple iterations of decoding912and conversion in the Convert to Bit LLRs circuit906(as in step4413ofFIG.35B) of symbol to bit LLRs (in the soft demapper circuit907) is known as the iterative decoding principle or the turbo principle [1].

Terminology: Types of LLRs

While the generation of one set of N bit LLRs may go through several iterations of demap-decode (due to consecutive CRC failures as in step4421ofFIG.35B), generation of another set(s) of bit LLRs may pass (see step4422ofFIG.35B) CRC in the first round and need no iteration. In any round of demap-decode, either the first round with no feedback (as in step4412ofFIG.35B), or the proceeding rounds (as in step4421ofFIG.35B) with feedback (as in step4418ofFIG.35B), the values on signals between the decoder circuit912and soft demapper circuit907can be classified into three types: apriori LLRs, extrinsic LLRs, and aposteriori LLRs. Apriori LLRs are always inputs and contain pre-existing information with respect to the circuit they are fed into. Extrinsic LLRs are always outputs and contain new information based on the calculation performed in the circuit they are generated from. Aposteriori LLRs are also outputs but are representative of all information (including both the pre-existing and new information) from the first iteration and for all symbols for the same code.

Decoder core915with soft output typically generate aposteriori information in the form of aposteriori LLRs914as outputs. This could be in the form of using internal memory to update their state of the message information over time, hence collecting all information. It could also be that what decoders receive as input are themselves representative of all information, and hence so the decoder's output in the form of aposteriori LLRs914. Although the decoders typically output aposteriori LLRs, as shown914inFIG.9, the type of data exchanged between the decoder912and the soft demapper circuit907is extrinsic LLRs. If this were not the case, and the two circuit interchanged aposteriori data, there would have been positive feedback causing the data to diverge and become corrupt [1].

A Dataflow Example

One example of how the three different types of LLRs flow between the decoder912and the soft demapper circuit907is shown inFIG.9and also explained with respect toFIG.35A. Assume that a new code is received4402at the receiver chain901: code A. As in step4409ofFIG.35A, the demodulator118has generated a set of N/4×16 correlator output magnitudes109of code A, and at step4410ofFIG.35A, the same number of OS LLRs is output from the demapper circuit113that is arranged to convert the correlator outputs to OS symbol LLRs. The Convert to Bit LLRs circuit906receives these OS symbol LLRs908as apriori data and converts (as in step4413ofFIG.35B) them into N bit LLRs909. The Convert to Bit LLRs circuit906also stores4411a copy of the OS LLRs in its internal memory, to reuse (as in step4419ofFIG.35B) them in case iterative decoding4421is applied. At this time (represented by step4412ofFIG.35B), the feedback bit LLRs910contains no LLRs (or equivalently carries N zero-valued LLRs) as this is the first time that data corresponding to code A is processed in the soft demapper circuit907. Generally, in some examples of the invention, the Convert to Bit LLRs circuit906generates its output based on both the received N/4×16 OS symbol LLRs908and the N feedback bit LLRs910received from the feedback line. Given that the feedback bit LLRs910are representative of pre-existing data—even though zero-valued (as in step4412ofFIG.35B) in the first iteration—the output of the Bit LLR circuit906will be N bit LLRs909of the aposteriori type.

Since the decoder912provides feedback bit LLRs910, and there is a closed-loop flow of data between the decoder912and the soft demapper circuit907, the decoder912expects extrinsic type of input LLRs as part its processing, as opposed to aposteriori LLRs, which would create a positive feedback loop [1]. Therefore, the soft demapper circuit907, which is setup in the closed loop with the decoder912, subtracts the feedback N apriori bit LLRs910from the N aposteriori bit LLRs909output of the Convert to Bit LLRs circuit906to generate N extrinsic bit LLRs904, as in step4414ofFIG.35B. As mentioned, the first iteration (as in step4412ofFIG.35B) of the Convert to Bit LLRs circuit906for code A, will have zero-valued apriori bit LLRs for the feedback bit LLRs910, and hence the extrinsic bit LLRs904output from the soft demapper circuit907will be equal to the output of the Convert to Bit LLRs circuit906in the first iteration. The demapper's extrinsic bit LLRs904output from the soft demapper circuit907is de-interleaved (π−1) in the decoder912and is fed into the decoder core915subsequently as N apriori bit LLRs916of the apriori type for the decoder912. Following this, the decoder912can use these apriori LLRs916to generate a vector of decoded bits917, which are then fed to the CRC decoder911shown inFIG.9. If the CRC decoder911check passes4422, then the transmission process is completed successfully.

InFIG.35B, if the decoder912fails (see4421) due to CRC decoding911for code A and decides to apply iterative decoding, the decoder912can generate a sequence of N feedback bit LLRs910. The result, for example the LLR feedback output in the form of aposteriori LLRs914of the decoder core915will be aposteriori, and for the same reason explained earlier to avoid positive feedback in the closed loop with the soft demapper circuit907, the decoder912subtracts the input apriori bit LLRs916from decoder core's915aposteriori LLR feedback output914. The resulting sequence of N extrinsic LLRs918are interleaved (π) and fed back (as in step4418ofFIG.35J) to the soft demapper circuit907as apriori feedback bit LLRs910through the feedback line, which will possibly have non-zero values this time. The Convert to Bit LLRs circuit906then uses the feedback bit LLRs910for a second OS-symbol-to-bit LLR conversion (as in step4413ofFIG.35B) for code A, when it also uses (as in step4419ofFIG.35B) the code A's OS LLRs that it had stored4411in its memory from the previous iteration. The Convert to Bit LLRs circuit906will still keep the same OS LLRs in its memory, as the CRC decoder911may fail4421for a second time and a third OS-symbol-to-bit LLR conversion (as in step4413ofFIG.35B) may be required. The end of the iterative decoding is decided by the decoder912, which may occur due to, either a CRC pass (resulting in a successful transmission process), or reaching a maximum number of decoding iterations (whereupon the transmission process is abandoned and deemed unsuccessful). Following the processing of code A, the next block of code (code ‘B’), may have been received4402in the receiver chain901, and the above process will be repeated for code B.

Calculating Bit LLRs

In the earlier description of conversion to bit LLRs for an OS scheme, it was explained how OS symbol LLRs908are generated from the correlator output values. where, it is explained how bit LLRs909are calculated (as in step4413ofFIG.3) from the apriori OS symbol LLRs908and the fed-back apriori bit LLRs910in an iterative decoding approach.

No Feedback from the Decoder

First, in a scenario where there is no feedback bit LLRs910from the decoder, or equivalently, the apriori feedback bit LLRs910to the soft demapper circuit907have values of zero, a 4-bit message m exists for which k=4 bit LLRs need to be found; one bit LLR for each of the message bits. In some examples, the 4-bit message can have any value from the set of 4-bit binary combinations {0000, 0001, . . . , 1111}3701, as shown at3700inFIG.28, which illustrates 4-bit message combinations and corresponding OS symbol LLR parameters. The following parameters may also be used to represent the bit LLRs of the message: LLR3bit,out, LLR2bit,out, LLR1bit,out, LLR0bit,outwhich correspond in the same order from the most significant bit (MSB) to the least significant bit (LSB) of the message. For inputs to the Convert to Bit LLRs circuit906, there is M=16 OS symbol LLRs values, where each value corresponds to one of the M=16 message combinations3702. The OS symbol LLRs3703are represented using the parameters LLR0sym,in, LLR1sym,in, . . . , LLR15sym,in, as listed inFIG.28. Therefore, on one side there exists M=16 OS symbol LLR values corresponding to M=16 message combinations, and on the other side the M=16 symbol LLRs are to be used to obtain k=4 bit LLRs for the 4-bit message.

Calculation

Referring now toFIG.29, terms in a bit LLR calculation are illustrated at3800, when there is no feedback from a decoder, according to some example embodiments of the invention. Here, the calculation (as in step4413ofFIG.35B) of the bit LLRs is better understood using an example. If it is assumed that LLRisym=LLRisym,infor all i in the range [0,M−1] or [0,15]. If bit0(LSB) of the transmitted message was a “1”, like messages “0001” or “1011”, it may be expected that the demodulator118will express a collective probability of all the message combinations with their LSB equal to “1” (column 8 inFIG.29) that is relatively higher than the collective probability of the message combinations with their LSB equal to “0” (column 7 inFIG.29), when the channel conditions are favourable. In the same way, if bit1of the message had a value of “1”, like “0010” or “1111”, it may be expected that the demodulator118will express a collective probability of all the message combinations with their bit1equal to “1” (see column 6 inFIG.29) that is higher than those with their bit1equal to “0” (see column 5 inFIG.29). The same can be said for the cases when message bits were “0”. The decoder is not aware of the actual values of the message bits, but attempting to determine them by considering combining probabilities (represented in LLRs) in a way that leads to a final message combination that is most likely amongst all to be the actual message. The left-hand side columns3801ofFIG.29show the different message combinations for the 4-bit message (k=4). The right-hand side columns3802show how each of the M=16 symbol LLRs relate in the bit LLR calculation (as in step4413ofFIG.35B) for each bit of the 4-bit message.

In order to calculate (as in step4413ofFIG.35B) the bit LLR value for one bit, the M=16 symbol LLRs is first divided into two groups of 8 LLRs each, according toFIG.29. For each bit of the 4-bit message (with an index in the range [0,k−1] or [0,3]), there are M/2=8 message combinations where the same bit index has a value “1”, and M/2=8 combinations where the same bit index has a value “0”. For example, for bit3(MSB) of the 4-bit message, message combinations {0000, 0001, . . . , 0111} has their MSB equal to “0”, and message combinations {1000, 1001, . . . , 1111} has their MSB equal to “1”. The LLR values corresponding to the first and second sets of 8 message combinations of the message's MSB are therefore {LLR0sym, LLR1sym, . . . , LLR7sym} and {LLR8sym, LLR9sym, . . . , LLR15sym}, and are shown in columns 1 and 2 ofFIG.29, respectively. It is also known that the logarithmic-likelihood ratio is defined as:

LLR=Δln[P⁢r⁢(bit=0)P⁢r⁢(bit=1)],[4]
and that multiplications and divisions in the probability domain correspond to additions and subtractions in the LLR domain, respectively. Therefore, the bit LLR value, for instance for bit3, will be the LLR probability of bit3when it is “1” subtracted from LLR probability of bit3when it is “0”, that is LLR3bit,out=L3bit,0−L3bit,1as shown3803inFIG.29.

In order to calculate each of the parameters L3bit,0, L3bit,1, L2bit,0, . . . , in the bottom row3804ofFIG.29, the corresponding LLR values need to be combined, which will be LLR parameters in column 1 for L3bit,0as an instance. To obtain the probability of the event that any message combination amongst a given set of combinations occurs, the probabilities of all combinations within the given set must be added. This is because the message combinations are mutually exclusive—only one message combination is the actual value of a 4-bit message. For instance, the probability that the message is one of the values {“0000”, “0101”, “1010” } is Pr[“0000” U “0101” U “1010” ]=Pr[“0000” ]+Pr[“0101” ]+Pr[“1010” ].

The addition of probabilities can be approximated using the Jacobian logarithm in the LLR domain. The Jacobian logarithm is:
log(a+b)=max(log(a),log(b))+log(1+e−|log(a)−log(b)|),  [5]and assuming a and b are mutually-exclusive probabilities, they can be combined using their LLR values by writing log(a+b)=max(LLRa,LLRb)+. This operation is called the 39axstar operation, and the signis used to denote it. The addition of probabilities can be represented using the 39axstar operator in the LLR domain. For instance, L3bit,0is defined as L3bit,0LLR0symLLR1sym. . .LLR7sym, which contains the terms in column 1 ofFIG.29. In the same way, other parameters L3bit,1, L2bit,0, . . . can be calculated from their corresponding columns inFIG.29.

With feedback from the decoder

As explained previously, for every 4-bit message (k=4) there are k=4 apriori feedback bit LLRs910fed back from the decoder912to the Convert to Bit LLRs circuit906, as shown inFIG.9. The following parameters are used to represent the apriori soft bits910(feedback bit LLRs), LLR3bit,inLLR2bit,in, LLR1bit,in, LLR0bit,inwhich correspond, in order, to MSB down to LSB of the message.

Given that the soft demapper circuit907is now receiving multiple inputs, they can be enumerated according to the following:the M output magnitudes of the correlators referred to as the first set of inputs to the soft demapper circuit907. These are used to generate a first set of apriori soft signals comprising M OS symbol LLRs908;the second set of inputs to the soft demapper circuit907may be defined to be a second set of apriori soft signals comprising the k feedback bit LLRs910(or k soft bits).

Now the question is how these k=4 new values of soft bits or feedback bit LLRs910can be used, along with the previous M=16 OS symbol LLRs908, to improve the k=4 feedback bit LLRs909as the output of the Convert to Bit LLRs circuit906. InFIG.28, it was shown that each of the M=16 OS symbol LLRs908represent a probability corresponding to one, out of M=16, of the message combinations shown in the left-hand side columns3801ofFIG.29. However, in this scenario, what is the relation of the k=4 apriori feedback bit LLRs910with respect to each message combination? In some examples, it is known that a positive LLR means the bit is more likely a “0”, and a negative LLR means it is more likely a “1”. Now, if the actual message was “0001”, it may be expected that the apriori LLRs corresponding to bits1-3were positive, and the LLR value corresponding to bit0was negative. Therefore, the value LLR3bit,in+LLR2bit,in+LLR1bit,in−LLR3bit,inmay be expected to be positive for the message “0001”. In the same way, it can be said that if the message, for instance, was “1010”, it may be expected that the following is positive: −LLR3bit,in+LLR2bit,in−LLR1bit,in+LLR0bit,in. This explanation can hold for all M=16 message combinations: if a message combination is the actual received message, its corresponding apriori bit LLR combination is expected to be positive.FIG.30illustrates at 3900 4-bit message combinations, with corresponding OS symbol LLR parameters and apriori bit LLR combinations. The apriori bit LLR combinations3901are listed and the division by 2 in each combination is required for the calculation (as in step4413ofFIG.35) of output bit LLRs.

The above explanation can be taken further by saying that if a message combination is the actual message, its corresponding apriori bit LLR combination is expected to have the largest value amongst all other bit LLR combinations3901. Therefore, by calculating the M=16 apriori bit LLR combinations of the left-hand side columns3801ofFIG.29andFIG.30, the largest of which directs the decoder to the message combination where according to the k=4 bit LLRs has the highest chance of being the actual message.

So far, in some examples and for the M=16 message combinations of the left-hand side columns3801ofFIG.29andFIG.30, there are two sets of M=16 soft signals that demonstrate the likelihood of the message combinations to represent the actual 4-bit message; the symbol LLRs3703and the apriori bit LLR combinations3901, as shown inFIG.30. As both sets are in LLR representation, they can be added together to become a single set of LLRs corresponding to the message combinations. Therefore, if the symbol LLR parameters inFIG.30are defined to be LLRisym=LLRisym,in+LLRisym,bit for all i in the range [0,M−1] or [0,15], the bit LLRs generated by the Convert to Bit LLRs circuit906can then be calculated using the methodology explained forFIG.29. The resulting (as in step4413ofFIG.35) bit LLRs909are aposteriori LLRs, as they represent all information, including both the newly received OS symbol LLRs908from the upstream and the old apriori feedback bit LLRs910received from the decoder912.

Summary

In summary, the demapper circuit113and Convert to Bit LLRs circuit906have been described that are arranged to convert the correlator outputs to OS symbol LLRs and for generating (as in step4413ofFIG.35B) aposteriori soft bit LLRs909, wherein a second set of apriori soft signals comprising k soft bits910are provided as a second set of inputs to the circuit. Furthermore, the circuit combines all apriori soft signals to generate a set of 2kaposteriori soft signals and wherein the set of 2kaposteriori soft signals are combined to obtain the set of aposteriori soft bit LLRs909. Finally, the aposteriori soft bit LLRs909are combined with the soft bits or feedback bit LLRs910of the second set of apriori soft signals, in order to obtain a set of extrinsic bit LLRs904comprising k soft bits, as in step4414ofFIG.35B.

The flow4400of the soft demapper for the OS scheme, as demonstrated in the flowchart ofFIG.35AandFIG.35B, may be summarised as follows. Initially at step4401, the demapper receives4402a set of signals as the outputs of the demodulator that correspond to a transmitted frame. Then, in some examples, it is decided which of the three different methods of estimating the distributions of the correlator outputs, are to be employed. If the offline method is chosen at step4423, the characteristics of the channel, such as the signal-to-noise ratio and channel type (e.g., AWGN), are identified in the offline channel estimation method4405. The result of the identification is then used to address a pre-computed table, say pre-computed table112ofFIG.1, of distribution parameters of the correlator output magnitudes. If, in contrast, an online method of distribution estimation is chosen at step4424, the choice will be either the use of synchronisation sequences in the first online channel estimation method at step4407, or applying the largest magnitude method of the second online channel estimation method at step4408following a decision at4404. Whichever estimation method is employed, the estimated parameters of the magnitude distributions, alongside the collected correlator output magnitudes of step4409ofFIG.35A, are used to calculate the OS soft signals of step4410ofFIG.35A, which may be in the form of LLRs, as described previously in the section that calculates OS LLRs.

Following that, the calculated4410OS soft signals are stored4411in internal memory, in case a demap-decode round of iterative decoding is required later. In a first iteration of a possible iterative decoding process, there is no value on the feedback path from the decoder. Hence, this feedback will be all zero-valued, as in step4412ofFIG.35B. Next, the aposteriori soft bits, in the form of LLRs (the OS bit LLRs), are calculated, as in step4413ofFIG.35B. This is followed by converting (as in step4414ofFIG.35B) the aposteriori soft bits into extrinsic sift bits as the type required by, and sent (step4415) to, the decoder. After the decoder is finished with the process of decoding the extrinsic information that it received, the demapper receives (step4416) the CRC status and the decoded soft bits from the decoder. Following a check of the CRC status at step4417, if the check failed4421, turbo decoding may be applied in some examples of the invention and the received4416decoded soft bits may be treated (as in step4419ofFIG.35B) as apriori information fed back from the downstream decoder. Also, the OS soft signals, which had been calculated at step4410previously, are loaded (as in step4418ofFIG.35B) from memory as another set of apriori information for the calculation of soft bits. The symbol-bit LLR conversion uses all apriori information to make a second calculation at step4413of soft bits as bit LLRs. The demap-decode iterations of turbo decoding are ended at step4422either by a CRC pass or by abandoning further decoding operations of the current frame, which may lead into, for example, a hybrid automatic repeat request (HARQ) re-transmission. At this point, the demapping of the current frame is ended at step4420.

Testbench

Previously a method of providing the decoder912with extrinsic bit LLRs904of the received message, for example in the form of soft bits, has been described, by converting4413OS symbol LLRs908to bit LLRs909using the iterative decoding principle. Here, an evaluation of the conversion to extrinsic bit LLRs904is described. Different approaches can be used to evaluate the result of the Convert to Bit LLRs circuit906—which is also the extrinsic bit LLRs904output of the soft demapper circuit907. One approach is to observe the bit error rate (BER) when using the soft decision demapping, and have it compared against the BER of the same transmission but with a hard demapping of correlator output values into hard bits instead of LLRs. Other approaches characterise the generated extrinsic bit LLRs904from the soft demapper circuit907, such as measuring mutual information (MI) histogram and measuring mutual information averaging, which is described later.

It is noted that, as some of the above approaches rely on comparisons of the decoded bits919against transmitted bits920, they are only applicable in lab-environment testbenches where transmitted bits920are accessible, as opposed to real systems where only received4402data921are in hand. Examples of these lab-only evaluation methods are BER and MI histogram measurements. However, although these methods are only applicable to lab environments, they provide a good measure in evaluating the quality of the extrinsic bit LLRs904output of the soft demapper circuit907.

High-Level Operation

A testbench was developed in Matlab to measure the quality of the extrinsic bit LLRs904output from the soft demapper circuit907when it uses the above-described conversion to bit LLRs. A block diagram of the testbench1000for the 16OS scheme (orthogonal signalling with M=16, and the datasets taken from [5]) is illustrated inFIG.10. As shown, there are no encoders or decoders: instead, encoded bits1001are simulated using random bits from a uniformly distributed random number source1002. Also, the data that is meant to be fed into a decoder in real systems, is generated from the soft demapper circuit907, and goes through a few measurements (such as BER1005and MI calculations1006,1007). The results of the measurements are used by a final processing function1008, which plots an EXIT chart. InFIG.8, the blocks and flows in the set113,114,906,907,1001,1004,1013,1020,1023,1024,1026,1031,1032and1035correspond to parts of the testbench that are in common with a real transmitter-receiver communication system900. The remaining blocks and flows are only specific to the lab testbench and are typically absent from real systems.

Every execution of the testbench is based on the choice of a true channel and an estimated channel and generates one EXIT chart1011. In a practical system, it may not be possible for the receiver chain901to perfectly estimate the channel characteristics. This is represented in the testbench ofFIG.8by using a first channel model (referred to as the true channel model1009) to simulate the transmission and by using a second (potentially different) model1010to provide the channel characteristics that are obtained by the (potentially imperfect) channel estimator. Indeed, in some applications, channel estimation may be entirely absent from the receiver chain901and a fixed set of (worst-case) channel characteristics may be assumed, irrespective of what the true channel characteristics are. The user of the testbench will choose the two channel models as inputs1009,1010, as shown in the right-hand side inFIG.8, and will expect the output EXIT chart1011that is specific to the two chosen channels, as shown in the left of the block diagram. The testbench1000was applied to several true and estimated channel combinations, with their EXIT charts illustrated inFIG.10toFIG.12. In these figures, each EXIT chart has the same true and estimated channel types, but sometimes different SNRs. The true and estimated channels in each EXIT chart are both based on either the additive white Gaussian noise (AWGN) model, or the multipath (MP) model. WhileFIG.10has a different SNR between its true and estimated channels,FIG.11AandFIG.13BandFIG.12have the same SNRs each.FIG.9is an example EXIT chart when both true and estimated channels are based on the AWGN_-7.5 dB dataset.

Mutual Information

EXIT charts demonstrate a characterisation of the unit that they were generated from, which could be a demapper, or a decoder. In examples described herein that seek the evaluation of a soft demapping model, all EXIT charts1100,1200,1300,1400illustrated inFIGS.11-14belong to soft demapper circuit907. In these charts, the X axis is the mutual information of the apriori bit LLRs1013: which is a measure of the quality of the feedback (which would come from the channel decoder in a real system, but which comes from a Generate random LLRs circuit1014in the testbench1000) and fed into the Convert to Bit LLRs circuit906. The mutual information between two random variables quantifies the amount of information in one variable that can be obtained by observing the other variable, and MI values are in the range [0,1]. An MI value of MI=0 means that there is no feedback from the decoder, and the feedback line will comprise a sequence of N zero-valued bit LLRs. Any value of MI>0 corresponds to cases when feedback from the decoder is present, and larger values of MI mean the feedback values have greater quality. The Y axis in the EXIT charts1100,1200,1300,1400illustrated inFIGS.11-14, is also a quality measure between zero and one, and corresponds to the output value generated by the soft demapper. In each of these EXIT charts1100,1200,1300,1400in this document, there are three plots1101,1102,1103and one point shown by a cross1104, and some numberfigures1105on the maximum coding rate achievable for the soft decision approach.

Testbench Flow

To generate the EXIT chart data, the testbench loops over a number of test indices1016, where each1017corresponds, in the EXIT chart1011, to points in all plots with the same MI value1018for the apriori bit LLRs1013. For each test index1017of the test indices1016, an apriori MI value1018is chosen. The system is simulated using this MI value1018, demapper output of N extrinsic bit LLRs1004quality parameters (such as BER1005) are measured, and eventually some points in the EXIT chart1011are identified. For example, the execution of the testbench with MI=0.3, produces one point for each of plot1101, plot1102and plot1103in the EXIT chart1100inFIG.9.

Transmitter

In each simulation with a test index1017, encoded bits1001are generated from a uniformly-distributed random number source1002and fed into the bits to symbol circuit114, which converts bits into symbols based on a modulation with 16 symbols (M=16). Hence, for N encoded bits1001there will be N/4 symbols (k=4)1020to be modulated.

Datasets

The models used in this testbench for different modulation schemes and channel types are represented by the datasets that were included in the previous correlator distribution investigation and conversion to symbol LLRs for an OS scheme, with the addition of one extra dataset. These datasets are provided by [5] and are presented using the following notations: AWGN_100 dB, AWGN_0 dB, AWGN_−3 dB, AWGN_-7.5 dB, MP_0 dB, MP_−3 dB, MP_-7.5 dB. Each of these datasets is a large matrix of 8100 rows, where each row contains (a) a symbol value3502in the range [0,M−1] or [0,15], and (b) M=16 correlator output magnitudes3503, which were generated for that symbol value from the mathematical model of the corresponding modulation scheme and the channel. A memory1012inFIG.8stores and replicates all the above datasets, with in this example there being seven datasets in total.

Models of True and Estimated Channels

In this example, the choice of the true channel1009will select one of the available seven datasets and treat that as the modulation-channel simulation model1021of the testbench1000. During the testbench simulation, for every symbol value generated from the Bits to Symbols circuit114ofFIG.1, a row with the same symbol value from the Chosen Modulation & Channel Model1022ofFIG.10is randomly chosen and the corresponding 16 correlator values in the same row are output1023. In this way, the correlation between the correlator outputs is preserved. Note however that the memory in the channel between successive transmissions is not modelled by this approach. However, this is justified because the proposed soft decision demodulator does not exploit this memory and because the spreading sequences used in [5] are designed to mitigate the dispersion that causes this memory. The user also needs to choose the estimated channel. In this example, the Data Analyser circuit1024ofFIG.10fits Rician distributions to all rows from the user-chosen1010estimated dataset1025and returns four parameters1026corresponding to the distributions of the correct and incorrect sets of correlator data, as defined and explained in the previous correlator distribution investigation and conversion to symbol LLRs for an OS scheme. While in this testbench1000, the channel estimation task of the Data Analyser circuit1024is performed concurrent to the simulation, in real systems it could take place online by analysing the received signal, or it could take place offline using very large sets of channel data if a fixed set of channel characteristics is to be assumed. Performing channel estimation offline can avoid large on-line complexity overheads.

Hard Decision Flow

In the testbench1000a hard decision flow1027of the receiver1028was included in order to calculate its BER1029and compare it against the BER performance1030of a soft decision bit LLR model. The Hard Decision for Symbols circuit1031receives M=16 correlator values from the channel model and performs a hard decision by taking the index1034of the largest correlator value as the 4-bit message value (k=4), which then passes the index to the Convert to Bits circuit1032to generate the equivalent bits1035. For instance, if the largest correlator value amongst the M=16 has an index of ‘9’, the result of a hard demapper circuit1033for this symbol will be “1001”. When the hard demapper circuit1033demapped all symbols into bits, the BER measurement circuit1036compares those bits with the true encoded bits1001from the transmitter chain1037and calculates the BER value using the formula BER=number of bits in error divided by the number of all bits. The BER value1029will be a fractional number in the range [0,1]. While smaller BERs represent better performance, the value 1-BER is plotted in the EXIT chart (using a cross point “X”) so that it can be compared against other quality measures, whose larger values also demonstrate better performance, and similarly have values between zero and one. The hard decision flow1027is independent from the soft decision flow1038and its iterative feedback, and for every dataset there will be just one hard BER value10291104. Also, since the hard decision flow1027does not use feedback, its BER1029can be compared against the case in the soft decision flow when there is no feedback. Due to this, the hard decision BER point is plotted1104at apriori MI=0 in the EXIT charts, as shown inFIG.11.

Quality of LLRs

The soft decision flow1038is based on receiving apriori bit LLRs1013as feedback to the soft demapper circuit907, as shown inFIG.10. Instead of using a decoder model in the testbench, it is sufficient for the purpose of the testbench that the apriori bit LLRs1013to the soft demapper circuit907are generated differently from a decoder, which in this case are generated by the Generate random LLRs circuit1014. In order to simulate a real receiver, the generated apriori bit LLRs1013must be, with a certain quality, similar to the encoded bits1001, and hence the Generate random LLRs circuit1014takes as input the true encoded bits1001in order to create LLRs with such a similarity. These random LLRs are generated according to a Gaussian distribution, as described inFIG.4of [1].

Feedback Apriori Bit LLRs: A Function of MI in this Testbench

The quality of the feedback apriori bit LLRs1013in the testbench1000ofFIG.10are represented by the MI value as the mutual information. The MI is a statistical measure that can be used to represent how much the demodulated extrinsic LLRs (or feedback apriori bit LLRs1013) have shared information with respect to the true encoded bits1001. Therefore, as a statistical measure it can be expected that the MI value is generally calculated to find out the dependence between the encoded and demodulated bits, for example, in lab-environment testbenches where decoder models are present. However, in this example testbench1000, since feedback apriori bit LLRs1013are not generated from a decoder model, but as a function of the true encoded bits1001, the MI of the encoded and demodulated bits may be defined for different test indices. An MI value1018will become an input to the Generate random LLRs circuit1014to generate mimicked decoded bits. The Generate random LLRs circuit1014contains a mathematical function that produces LLRs whose mutual information with respect to the given input bits is equal to the input MI value. The Generate MI value circuit1039uses a test index1017to generate an MI value1018in the range [0,1].

Soft BER Calculation: Why Aposteriori Data?

Calculating the bit error rate in the soft decision flow1038requires aposteriori information from the soft demapper-decoder interface. This is because the output of a soft decoder is typically aposteriori data (as explained previously in the section describing a conversion to bit LLRs and exit chart evaluation for an OS scheme) and hence the aposteriori information would be the closest form to that of a decoder's output. Obtaining aposteriori data from the decoder-demapper interface is shown inFIG.10with the summation1040of the N extrinsic bit LLRs1004and N apriori bit LLRs1013from and to the soft demapper circuit907, respectively. The testbench1000then makes a hard decision1041on the aposteriori bit LLRs1042to provide N bits1043from the N aposteriori bit LLRs1042. Here, a hard decision of binary ‘0’ is made for positive aposteriori LLRs and a hard decision of ‘1’ is used for negative LLRs. The resulting N bits1043are compared in terms of BER1005against the true N encoded bits1001from the transmitter chain1037in order to provide a BER measure1030for the test index1017and the MI1018of that execution.

Comparison of Soft and Hard BER Values The comparison of the soft BER value when MI=0 against the hard BER1029is valid as both values are calculated based on bits resulting from the same type of (extrinsic) data from their demapping of symbols to bits. This is obvious with the hard decision flow1027, as there is no feedback in this flow, and hence the data from the hard demapper circuit1033is extrinsic. Also, since the soft decision flow1038when MI=0 has its feedback apriori bit LLRs1013with values of zero, the aposteriori bit LLRs1042, in this case, are equal to the N extrinsic bit LLRs1004generated from the soft demapper circuit907, as shown inFIG.10. Therefore, a decrease in BER value from the hard decision flow1027to the soft flow1038means that the use of LLRs has been able to increase the quality of the receiver by reducing the error rate. In the EXIT chart, this BER performance increase will be the case if the cross point1104of the hard BER value happens to be underneath the plot1101of the soft decision BER value—as the plot values are in the form of 1-BER. Generating soft decision BER values also shows the effect of increasing the quality of feedback LLRs on the BER performance. For example,FIG.11demonstrates that using the example soft demapping approach, the quality of LLRs to the decoder increases as the quality of the feedback LLRs enhances. It may be observed that the soft (decision) demapper circuit907produces an equal or better BER than the hard (decision) demapper circuit1033in all cases shown inFIG.12toFIG.14B.
MI: Histogram and Averaging Methods

There are two other quality measures that can characterise a soft demapper circuit's output: the mutual information histogram1102and the mutual information averaging1103, as demonstrated in the EXIT charts. Both measures provide mutual information, and both look at the output N extrinsic bit LLRs1004of the soft demapper circuit907to calculate their MI, as opposed to the BER measure that required aposteriori bit LLRs1042. The histogram method (equation (2) in [1]) provides the mutual information1044between the N extrinsic bit LLRs1004and the true encoded bits1001from the transmitter chain1037. This known method does not assume that ‘LLR values could not be wrong’—i.e., this known method does not trust LLRs—and checks those LLRs against the true encoded bits1001. In contrast, the averaging method (the equation immediately belowFIG.4in [1]) solely looks at the N extrinsic bit LLRs1004and provides a measure1045of quality without an outside reference. Thus, it provides a quality measure based on trusting LLRs. For this reason, the averaging method works best when the decoding algorithms are optimal.

Upper Bounds on Achievable Coding Rates

The MI histogram method can also be used to provide an upper bound on the achievable coding rate when the soft demapper circuit characterised by the MI histogram is used. The achievable coding rate between a decoder and a demodulator in general (which includes a demapper) is mainly dependent on the following two aspects. The first aspect is how well the decoder and the demapper are matched (see discussion of the ‘area gap’ in [1] for more details). Like demappers, the behaviour of a decoder can also be characterised using EXIT charts. One can compare a demapper's EXIT chart with that of a decoder to determine how the two match. The second aspect in identifying the achievable coding rate is the block length applied when using the system: shorter block lengths decrease the coding rate required in practice (see [1] for more details).

Regardless of whether a decoder's EXIT chart is present, or a code length is chosen, the demapper's EXIT chart contains figures that define the theoretical upper bound on the coding rate that supports reliable low-BER operation using the demapper. This upper bound differs between the cases when there is iterative feedback in1107and when there is no iterative feedback in1106inFIG.11. For example, according to the EXIT chart ofFIG.11, the apriori MI histogram value for when the feedback quality has an MI of zero is 0.87 (rounded towards zero). This means that the maximum achievable coding rate using the example soft decision demapping approach without an iterative feedback for an AWGN_7.5 dB channel is 0.87, shown at1106. In the presence of iterative feedback (when feedback has MI>0), the area below the MI histogram plot will be the maximum achievable coding rate [1]. InFIG.11, the area below the MI histogram plot is 0.92 (rounded towards zero), and hence the maximum achievable coding rate in the example soft decision demapping approach when there is iterative feedback for an AWGN_7.5 dB channel is 0.92, shown at1107

Results: EXIT Charts

High-SNR Charts

The EXIT charts in inFIG.12toFIG.14Bshow that for SNRs≥−3 dB in all channels (5 charts in total) there is not any bit error: all hard and soft BER values are zero. Also, the MI quality measures of the demapper's extrinsic LLRs, both histogram and averaging methods, have values of 100% for all MI values of the feedback quality. This verifies the soft decision demapping model according to example embodiments of the invention for normal high-SNR conditions: the model successfully provides bit LLRs to the decoder with 100% mutual information with the true bits sent from the transmitter, and with zero bit error rates.

Low-SNR Charts

Without Iterative Feedback: BER

The EXIT charts for small SNRs of −7.5 dB (for example the two charts in total1301,1401inFIG.13AandFIG.13BandFIG.14AandFIG.14Balso demonstrate that the soft decision demapping model according to example embodiments of the invention works as expected. The two EXIT charts of AWGN1301and MP1401channels inFIG.13AandFIG.13BandFIG.14AandFIG.14B, each with an SNR value of −7.5 dB, have 1-BER values of 0.97 (rounded towards infinity) for their hard decision, and have slightly better values for their soft decision when there is no iterative feedback. This means that the soft demapping model according to example embodiments of the invention managed to preserve the bit error rate when switching from the hard model to the soft model without feedback.

With Iterative Feedback: Effect of Feedback MI on BER and the Two MI Figures

The low-SNR EXIT charts1301,1401inFIG.13AandFIG.13BandFIG.14AandFIG.14Balso demonstrate that as the quality of feedback LLRs increases (when MI>0), the quality of the demapper circuit's output LLRs, as well as the bit error rate improve. The 1-BER figure approaches to 100% values when the feedback quality MI goes to the value of 1, as may be expected for the case where perfect information is fed back. Also, the demapper circuit's output quality MI figures of histogram and averaging methods, start from values between 85%-90% for feedback MI=0, and become approximately equal to 97% when the feedback MI is ‘1’. The two MI figures have also values close to each other for all feedback MI values, which means that the two MI measures represent the quality of the feedback LLRs confidently and show that the consistency condition of [1] is met.

Maximum Achievable Coding Rates

Depending on the decoder and the chosen block length, a receiver may be able to converge to parts of its demapper circuit's EXIT charts that has the highest quality values. In the illustrated EXIT charts, the fact that highest BER performance and MI quality values are both near 97%-100% when the feedback MI is equal to 1, means that the proposed demapper circuit approach according to example embodiments of the invention may provide the highest quality LLRs. The low-SNR EXIT charts1301,1401, ofFIG.13AandFIG.13BandFIG.14AandFIG.14Balso show that the upper bound on the coding rates are 0.88 and 0.93 (rounded to nearest 2nddecimal place), for the cases when the iterative feedback is absent and is present, respectively.

OS-PSK Soft Demapping and EXIT Chart Evaluation

In this section, a proposed soft-decision approach in demapping of a demodulator's correlator outputs from an orthogonal signalling-phase-shift keying (OS-PSK) modulation scheme into extrinsic soft bits in the form of logarithmic likelihood ratios (LLRs)—also known as bit LLRs or soft bits is described, according to some examples of the invention. The operation of one example of the proposed soft demapper is demonstrated in the flowchart ofFIG.36.

The previous section presented a soft demapping approach for OS-only correlator outputs. The functionality of some circuits inFIG.15Ahas been previously described with regard to earlier figures, so will not be repeated in order not to obfuscate the concepts described here.FIG.15Bintroduces the following additional circuits: a Convert to PSK symbol LLRs circuit1501and a Symbol-to-symbol LLR circuit1502. This section also explains how, in the transmission systems captured inFIG.15AandFIG.15B, the transmitter chain1503applies the direct-sequence spread spectrum (DSSS) technique (using the Spreading Sequence Generator circuit) and the modulator circuit1504to convert information bits into OS-PSK modulated data.

Modulation Model

Transmitter Chain

The OS-PSK modulation scheme, as shown inFIG.15AandFIG.15B, turns the N encoded bits1505into a number of OS symbols1506and PSK symbols1507according to the work done by [7]. Take the example when the OS scheme has a modulation parameter (referred to as the OS radix) of M=16 (also referred to as the 16OS scheme), which corresponds to k=log2M or 4 bits per OS symbol. Likewise, consider the case where the PSK modulation parameter (referred to as the PSK bits-per-symbol) has a value of Qm=2, which corresponds to a PSK radix (also known as modulation order) of 2Qm=4. A PSK Scheme with Qm=2 is Also called the quadrature PSK (QPSK). In every k+Qm=4+2=6 bits of an encoded message, in this example, 4 bits are turned into an OS symbol and 2 bits are converted into a PSK symbol, where both symbols have complex values. Assuming N is an exact multiple of 6, there will be a total of N/(k+Qm) or N/6 OS symbols1506and the same number of PSK symbols1507. In the transmitter chain1503, the OS-mapped k bits are referred to as the first set of bits1508and the PSK-encoded Qmbits are referred to as the second set of bits1509.

Note that in this section, in accordance with some examples of the invention, a fixed OS radix of M=16, but with a variable PSK radix, is employed. This approach enabled the inventors to explore the impact of varying the PSK radix. However, it is envisaged that, in other example embodiments, the soft-demapping methodology and concepts described herein can be applied to an arbitrary number of OS bits ‘k’.

Indeed, in the extreme, a skilled practitioner could envisage a scheme where the OS radix is M=1 and there are k=0 bits conveyed by the selection of a spreading sequence, and with all N=Qmbits being conveyed by the phase of the spreading sequence. In this scheme, the transmitter would repeatedly transmit the same spreading sequence, each time with a phase rotation that conveys Qmbits.

Direct-sequence spread spectrum

In order to improve the signal-to-noise ratio (SNR) at the receiver chain1510, the DSSS technique is used in the transmitter chain1503which multiplies1517the OS symbols1506by a pseudorandom base sequence of signals represented by complex numbers. The base sequence is called the spreading sequence, and each signal value in the spreading sequence1511is known as a chip. The chips have shorter durations compared to the encoded bits1505, and hence have larger bandwidths. The conversion of the information bits into chips has the effect of scrambling the information bits and widening their frequency spectrum, and hence reducing the overall signal interference when the signals are modulated. The receiver chain1510is aware of the spreading sequence and uses it to de-spread the received signal.

Modulation

In the example above, while k=4 bits1508amongst 6 bits of the encoded bits1505that are turned into an OS symbol1506and then turned into a spreading sequence1511of complex values, the other Qm=2 PSK-encoded bits1509are mapped1515into one (amongst four) possible complex PSK symbols1507in a quadrature PSK scheme. The four possible QPSK-mapped values have different phases, but the same magnitude, and are equally spaced in a complex plane. The PSK-symbol1507of the Qm=2 PSK encoded bits1509is multiplied, in the Modulator circuit1504, by each value in the spreading sequence1511. The result is a sequence1518of complex values, that is a rotated version of the spreading sequence1511corresponding to the k=4 OS-encoded bits1508, where the amount of rotation is according to the value of the other Qm=2 PSK-encoded bits1509. This final sequence1518of complex numbers contains the information for all the k+Qm=6 encoded bits1505and is transmitted through the antenna(s).

Data

The receiver chain1510will demodulate the signals based on its knowledge of the applied schemes, such as the PSK modulation order (from the value of Qm) and the spreading sequence1511. The datasets used in this work to evaluate the example soft demapping approach are provided in [6], and are based on a 16OS-QPSK scheme (that is with M=16, Qm=2), and were generated based on a spreading sequence with 50 chips. Subsequent sections propose a technique that allows these 16OS-QPSK datasets to be generalised and used to model any PSK radix. These datasets belong to the same set of channels and SNR values used previously: AWGN_100 dB, AWGN_0 dB, AWGN_−3 dB, AWGN_-7.5 dB, MP_0 dB, MP_−3 dB, MP_-7.5 dB. The AWGN and MP terms refer to the additive white Gaussian noise and the multipath channels, respectively. For example,FIG.31illustrates the AWGN_0 db dataset of the 16OS-QPSK scheme [6]: the table shows the first 20 rows out of 8100. Each dataset is comprised of 8100 rows, where each row includes the value4001of one 6 bits of the message and the corresponding M=16 pairs4002of in-phase (I) and quadrature-phase (Q) correlator values.

Magnitudes of Correlator I-Q Pairs

The I and Q values in each I-Q pair represent the real and imaginary parts of a complex correlator output value, respectively. For instance, in row 1 ofFIG.31, the first correlator output has a complex value of −0.9-6i, where i is the imaginary unit. The magnitude of an I-Q pair resembles the possibility of the pair's index value, with respect to other pairs amongst the 16 in a row (hence a cross-correlation value, like a correlator's output), to represent 4 bits of the original transmitted 6-bit message. Therefore, in a similar manner to the previously described example, the one pair amongst M=16 that has the largest magnitude, is generally expected (in favourable channel conditions) to have its index with a value that is the same as the k=4 least significant (LS) bits of the (k+Qm) 6-bit message. Here, the convention that the PSK bits provide the Qmmost significant bits and the OS bits provide the k least significant bits is adopted. It is envisaged that other conventions may equally be adopted within the inventive concepts herein described, such as the PSK bits providing the k least significant bits. The I-Q pairs with the largest magnitudes in their rows are underlined inFIG.31AandFIG.31B. For example, the 9throw4003inFIG.31Ahas its LS k=4 bits of message with a value 7 (‘0111’), and the index of the largest-magnitude I-Q pair in this row has also a value of 7. As discussed previously, in one row of a dataset, the I-Q pair with an index equal to the value of the LS k=4 bits of the message may be referred to as the “correct” correlator outputs, and the remaining M−1=15 1-Q pairs as the “incorrect” correlator outputs. To elaborate further, the ‘correct’ correlator output is provided by the correlator for which the corresponding one of the 2kpossible signals was selected as the transmission signal.

Phases of Correlator I-Q Pairs

While the magnitudes of the correlator I-Q pairs contain the information for k=4 bits of the (k+Qm) 6-bit message, the phase values of the I-Q pairs represent the information for the remaining Qm=2 bits. The datasets have their QPSK modulation on the most significant (MS) 2 bits of the message. For example, the 2 MS bits of the message in row 14004ofFIG.31is ‘01’, which in this example means this message is QPSK-modulated with a phase indexed as 1 amongst indices in the range [0,3](that is [0, 2Qm−1] in the general case). Depending on the QPSK coding, phase indices map to a particular set of Gray-coded phase values. For instance, with a phase mapping of [π/4, 3π/4, −π/4, −3π/4] to [00, 01, 10, 11], the phase index ‘01’ is mapped to the phase value 3π/4, and hence the correct correlator output I-Q pair in row 1 (FIG.31) is expected to have a phase with value 3π/4.

Hereafter, a technique that allows these 16OS-QPSK datasets to be generalised and used to model any PSK radix is described. For the sake of simplicity, in these illustrated examples, phase mappings that begin with a phase of 0 will be adopted, such as [0, −π] for binary PSK (BPSK), or [0, π/2, −π/2, −π] for QPSK, and so on.

Distribution of Correlator Outputs

Correlator Output Magnitudes: OS Modulated

Similar to the work in the earlier section that investigated the data from an OS-only model, the distributions of the correct and incorrect correlator output magnitudes for the OS-PSK scheme can be aggregated into two distributions, as the first and the second aggregated correlator output magnitude distributions, respectively. The parameters1520used to represent each of the two aggregated distributions may also be referred to as the first set of distribution parameters. As before, the Rician distribution may be identified as the single distribution which fits best to each of both the correct and incorrect sets of correlator output magnitudes. The distributions of correct and incorrect correlator output magnitudes, as described previously, are shown inFIG.21A,FIG.21B,FIG.21Cat2100andFIG.23A,FIG.23B,FIG.23Cat2300, for both the AWGN and the MP channel datasets, and an example1600is provided inFIG.16for the MP_-7.5 dB dataset. Comparing these distributions with those ofFIG.5A,FIG.5B, andFIG.5CandFIG.6A,FIG.6B, andFIG.6Cdiscussed earlier for the OS-only scheme shows that the data are distributed very similarly between the OS-PSK (this section) and the OS-only (earlier section) models. This is demonstrated through the Rician distribution's non-centrality parameter (s) and scale parameter (a), which have similar values of parameters303ofFIG.3for all SNR values between the two models and for the first set of distribution parameters.

In summary, as explained previously, methods for estimating the parameters of the first and second aggregated correlator output magnitude distributions have been described, wherein one of the following is employed:(i) the first set of distribution parameters of the first aggregated correlator output magnitude distribution are estimated (as in step4507ofFIG.36A) by fitting a first probability distribution to the magnitudes of correlator outputs obtained by correlating received synchronisation signals with known synchronisation signals;(ii) the first set of distribution parameters of the second aggregated correlator output magnitude distribution are estimated (as in step4507ofFIG.36A) by fitting a second probability distribution to the magnitudes of correlator outputs obtained by correlating received synchronisation signals with signals other than the known synchronisation signal;(iii) the first set of distribution parameters of the first aggregated correlator output magnitude distribution are estimated (as in step4508ofFIG.36A) by fitting a third probability distribution to the magnitudes of correlator outputs having the greatest magnitude among sets of 2k correlator outputs obtained by the bank of 2k correlators;(iv) the first set of distribution parameters of the second aggregated correlator output magnitude distribution are estimated (as in step4508ofFIG.36A) by fitting a fourth probability distribution to the magnitudes of the 2k−1 correlator outputs that do not have the greatest magnitude among sets of 2k correlator outputs obtained by the bank of 2k correlators; or(v) the first set of distribution parameters of the first and second aggregated correlator output magnitude distributions are selected (as in step4505ofFIG.36A) from, say, a look-up-table1527.

In other examples, it may also be demonstrated that the first, second, third or fourth probability distributions may be represented using the Rician distribution.

Correlator Output Phases: PSK Modulated

Phase Error Values

The aggregated distributions of the correlator output phase values are shown inFIG.22A-FIG.22CandFIG.24A-FIG.24Cfor the AWGN2200and Multipath2400channels, and for the correct and incorrect sets of I-Q pairs. The data in these figures are represented in terms of the phase errors: for every row in a dataset (likeFIG.31AandFIG.31B), the ‘true’ phase of the transmitted message (defined by the Qm=2 MS bits of the message) is subtracted from the phase of each correlator I-Q pair as the ‘received’ phases, producing received phase errors. Following this, in some examples, the phase error of the ‘correct’ correlator output was extracted from each row of the dataset to obtain the set of ‘correct’ phase errors, while the remaining M−1=15 phase errors from each row were pooled into the set of ‘incorrect’ phase errors.FIG.17provides histograms and fitted distributions1700of the correct phase errors1701and incorrect phase errors1702for the MP_-7.5 dB dataset.

Fitting Distribution to Phase Errors

The parameters of the aggregated correct correlator output phase error distribution may be referred to as the second set of distribution parameters. The correct phase errors1701,1703were best fitted1704to normal distributions that were constrained to have mean values of zero. This constraint was achieved by fitting the absolute values of the correct phase errors to half-normal distributions, so that the distributions are characterised by just a single spreading parameter σphase1705. This is shown inFIG.15Bwhere the σphaseparameter1523of the correct phase error distributions is sent to the Convert to PSK symbol LLRs circuit1501. The incorrect phase errors1702, as shown inFIG.22A-FIG.22CandFIG.24A-FIG.24Call have uniform distributions. This means that the incorrect phases contain no information and do not need to be characterised by any distribution parameters.

According to above, only the correct phase errors1703, and not the incorrect phase errors1702, will convey information from the correlation data. Hence, the distribution of all the correct correlator output phase error values may be referred to as the aggregated correlator output phase distribution. In this name, the term ‘correct’ is explicitly excluded, even though the distribution is built from the correct set of phase error values. This maintains a generic definition of the aggregated correlator output phase distribution. As before, the correct phase error is provided by the correlator for which the corresponding one of the 2kpossible signals was selected as the transmission signal.

In summary, an approach has been proposed, wherein the plurality of aggregated correlator output phase distributions is one, and the aggregated correlator output phase distribution approximates the aggregation of the 2kdistributions of the output phases of the 2kcorrelators when the corresponding one of the 2kpossible signals was selected as the transmission signal. An approach has also been proposed wherein the aggregated correlator output phase distribution is represented by a second set of distribution parameters and wherein the second set of distribution parameters comprises a spreading parameter σphase.

Estimation Methods

The above analysis assumes a testbench environment, in which prior knowledge of the true values of the transmitted bits1508and PSK encoded bits1509is available. In practical receivers, however, this knowledge will not be available and the distribution parameter σphase1523must be estimated in its absence. To obtain the distribution of correlator output phases, online (as in step4524ofFIG.36A) or offline (as in step4523ofFIG.36A) approaches are proposed. In the example proposed online approaches, a Data Analyser circuit1524is responsible for analysing the correlator outputs1525from the demodulator1519received at run-time, finding the best distribution to fit to the data, and calculating parameters1526of the distributions, with the benefits from real-time tuning of distribution parameters. This is shown inFIG.15Bwith the Data Analyser circuit1524and is detailed below. But first, some offline approaches are discussed, which use the library look up table1527of correlator distributions shown inFIG.15B.

Offline Estimation Approaches

As described earlier for the OS-only scheme, in an offline approach, the correlator distributions may be calculated offline in order to build a library look up table1527of distributions on all applicable modulation-channel combinations. For example, the datasets mentioned earlier can be used as models for different channel conditions and modulation schemes. A configuration-time switch1528, as demonstrated inFIG.15B, can be configured to choose (as in step4505ofFIG.36A) the right set of data based on the system's modulation scheme and channel type. This could be performed as an extension of any other channel estimation tasks performed by the demodulator1519. For example, the outputs of these channel estimation tasks could be used to index (as in step4505ofFIG.36A) a look-up table1527of pre-computed correlator output distributions. More specifically, an offline estimation of correlator distributions may be used to record correlator outputs for several channel conditions and used to build a look-up table of those distributions. Then, during actual data transmissions a channel estimation task identifies (as in step4505ofFIG.36A) the current state of the channel and chooses the corresponding distribution from the look-up table1527, as shown inFIG.36A.

Another offline approach option would be to use a single set of correlator distributions in all cases, irrespective of the varying channel conditions. This single set of correlator distributions could be recorded during a worst-case scenario, perhaps at the lowest SNR where reliable synchronisation can be achieved. When the channel conditions match this worst-case scenario, the use of the corresponding correlator distributions will ensure the best possible performance. When the channel conditions are better than this worst-case scenario, the chance of decoding success can be expected to increase, even though the assumed correlator distributions are pessimistic compared to the true distributions.

Online Estimation Approaches

Other than using datasets to estimate the distributions offline, two online approaches4507,4508are also proposed which calculate the distribution parameters in the early stages of a real data transmission, such as when a channel estimation task is in progress. The first online approach of step4507inFIG.36Ais through correlating received synchronisation signals with known synchronisation signals. Using this first online approach of step4507, as the receiver chain1510is aware of the true symbol phases, it can associate every set of M=16 correlator outputs1525of a transmission to its correct phase. This first online approach of step4507may be referred to as correlator distribution estimation using synchronisation sequences. Following distribution aggregation in the Data Analyser circuit1524, a distribution fitting approach (like the histfit function of Matlab) may be used to estimate (as in step4507ofFIG.36A) the σphaseparameter1523for the aggregated distribution. The correlator distribution estimation using synchronisation sequences has the advantage of being operated online, but also has its own disadvantages. One disadvantage is that synchronisation sequences, unless embedded in time-consuming channel estimations, are usually short sequences. Therefore, they do not always provide sufficient samples to obtain accurate distributions, which may lower the quality of symbol LLRs1529calculated using those distribution parameters. Aiming for sufficient accuracy requires long sequences, and that adds a time overhead to the channel estimation phase and increases power and bandwidth consumption.

The other approach in estimating distribution parameters, that is described earlier for the OS-only scheme, is the online approach of step4508ofFIG.36A, which considers the largest-magnitude correlator output. The online approach of step4508divides the correlator outputs1525into two groups of a) largest magnitude correlator outputs (each largest magnitude would be amongst M outputs of the same received message), and b) non-largest magnitude correlator outputs (M−1 outputs with non-largest magnitudes of the same received message, and for several transmission/messages). In the online approach of step4508, it is assumed that any largest-magnitude correlator output, amongst the M correlator outputs1525, corresponds to its transmitted sequence1518from the transmitter chain1503. Based on this assumption, the phases of the largest-magnitude correlator outputs should also correspond to the phase value of the transmitted sequences1518, and hence each should be representative of the Qmnumber of PSK encoded bits1509in the message. Therefore, like the offline estimation approach of step4523where the incorrect phase error values1702of the incorrect correlator outputs followed a uniform distribution, the phase error values of the non-largest-magnitude correlator outputs are expected to have a uniform distribution too. Consequently, the valuable distribution(s) in the largest-magnitude of step4508will be of the phase error values of the largest-magnitude correlator outputs. These values form the aggregated correlator output phase distribution, as mentioned earlier, but this time for the largest-magnitude of step4508. The calculation of the phase errors in the largest-magnitude of step4508is the same as that described earlier, except that the ‘true’ phase value (which was known in other approaches), is assumed to be the one phase in the phase mapping that is closest to the phase of the largest-magnitude correlator output. More explicitly, the phase error may be estimated as the difference between the phase of the largest-magnitude correlator output and the phase mapping that is closest to this.

Note that the largest-magnitude correlator distribution estimation approach of step4508could be further refined by completing a first estimation of the σphaseparameter1523then using these to calculate LLRs, as described later with respect to a soft-demapper circuit, before they are provided to a channel decoder912. The channel decoder912can then attempt to remove any errors in the LLR sequence and use a Cyclic Redundancy Check (CRC) decoder911to determine if it has been successful. If it has not been successful, then the channel decoder can provide feedback LLRs to the Data Analyser circuit1524ofFIG.15B. These LLRs can then be considered and may cause the classification of the correlator outputs between the largest-magnitude and not-largest-magnitude groups to be overridden. More specifically, a correlator output having the largest magnitude for a particular transmission may be swapped for another that does not have the largest magnitude, when forming the largest-magnitude group, if the feedback LLRs provide sufficiently-strong suggestion that the magnitudes do not reflect the correct transmission.

A definition for the aggregated correlator output phase distribution is now provided, which applies to all the estimation approaches as described above. The aggregated correlator output phase distribution approximates the aggregation of the M distributions of the output phases of the M correlators when the corresponding one of the M possible signals was selected as the transmission signal. The aggregated correlator output phase distribution is represented by the second set of distribution parameters.

Summary

In summary, methods for estimating the parameters of the aggregated correlator output phase distribution have been proposed, wherein one of the following is employed:the second set of distribution parameters of the aggregated correlator output phase distribution is estimated (as in step4507ofFIG.36A)) by fitting a fifth probability distribution to the phase errors of correlator outputs obtained by correlating received synchronisation signals with known synchronisation signals;the second set of distribution parameters of the aggregated correlator output phase distribution is estimated (as in step4508ofFIG.36A)) by fitting a sixth probability distribution to the phase errors of correlator outputs having the greatest magnitude among sets of 2k correlator outputs obtained by the bank of 2k correlators; orthe second set of distribution parameters of the aggregated correlator output phase distribution is selected (as in step4505ofFIG.36A)) from a look-up-table1527.

It has also been demonstrated that the fifth or sixth probability distributions may be represented using the Gaussian distribution.

In the following example embodiment, the soft demapping circuit1522uses the phases1530of the M=16 correlator I-Q pairs to obtain the logarithmic-likelihood ratio (LLR) representation of the possible phase values.

Soft Demapper

Like the soft demapper circuit907ofFIG.9andFIG.10, proposed earlier for the OS-only scheme, in this section every message bit requires an LLR value to feed a soft-decision decoder912. This means that for a (k+Qm)-bit message, there needs to be (k+Qm) bit LLRs generated by the soft demapping circuit1522, and hence for a total of N encoded bits1505transmitted there will be N extrinsic bit LLRs904that the soft demapping circuit1522outputs, as shown inFIG.15B. Since k=4 OS bits of information (out of a (k+Qm)-bit message) are conveyed through the correlator output magnitudes1531(as described earlier), feeding those magnitudes, as the first set of inputs to the soft demapper circuit, to the demapper circuit (Convert to OS LLRs circuit)113inFIG.15Bwill provide (as in step4510ofFIG.36A) M=16 OS symbol LLRs908, as the first set of apriori soft signals, corresponding to the M=16 correlator outputs1525. Also, as in the OS-only scheme discussed earlier, a set of OS apriori feedback bit LLRs910is fed back from the decoder912into the soft demapping circuit1522as the second set of inputs to the soft demapper circuit1522, wherein those feedback bit LLRs910may be referred to as the second set of apriori soft signals.

In this example embodiment, a soft demapping circuit1522is described that can operate in the generalised case of M-OS-PSK using any number of PSK bits per symbol Qm. However, in the following discussions, QPSK with Qm=2 will be used frequently as a specific example, in which case (k+Qm)=6.

Conversion to Bit LLRs

The generation of extrinsic bit LLRs904is performed using the Convert to Bit LLRs circuit1521(FIG.15B). In an example embodiment with no feedback from the decoder, as described previously, a Convert to Bit LLRs circuit906, was introduced that converted M=16 OS symbol LLRs908of a 4-bit message to k=4 bit LLRs. The Convert to Bit LLRs circuit1521in this current section needs to generate k+Qm=6 extrinsic bit LLRs904for a 6-bit message, comprising of LLRs corresponding to the OS modulation and LLRs corresponding to the PSK scheme, as described previously with respect to the modulation model. The Convert to Bit LLRs circuit1521in this section uses the same approach used by the similar circuit discussed earlier for the OS-only scheme, but this time it has, as inputs, information on the bits representing the phase of demodulator correlator outputs1525. This new information is the PSK symbol LLRs1532provided by the Convert to PSK symbol LLRs circuit1501, as demonstrated inFIG.15B, and it may be referred in some examples as the third set of apriori soft signals.

Below describes the Symbol-bit LLR circuit1533ofFIG.15B, which is responsible for generating the aposteriori bit LLRs909. This Symbol-bit LLR circuit1533takes input from the Symbol-to-Symbol LLR circuit1502ofFIG.15B, which is described further below and is responsible for combining LLR information from inputs provided by three sources, namely the LLRs of908,1532and1534. These three sources are the Bit-symbol LLR circuit1535(described below, which processes the feedback apriori bit LLRs910), the demapper circuit (Convert to OS LLRs circuit)113(which processes the magnitudes1531of the correlator outputs) and the Convert to PSK symbol LLRs circuit1501(which processes the phases1530of the correlator outputs).

Symbol-Bit LLR Circuit

Like the OS-only scheme, it may be assumed that every message combination in the OS-PSK scheme has a corresponding symbol LLR. With 6-bit messages, this makes a total of 26=64 symbol LLRs for one message in, for example, the 16OS-QPSK scheme: LLR63sym, LLR62sym, . . . LLR0sym. These values are shown inFIG.15Bby the line with the annotation showing the total number of symbol LLRs1529for an N-bit encoded bits1505from encoder101as N/(k+Qm)×M×2Qmor N/6×16×22symbol LLRs1529. The symbol LLRs1529are converted (as in step4513ofFIG.36B) to bit LLRs909using the Symbol-bit LLR circuit1533inFIG.15B.FIG.32is an extension ofFIG.31, which applied for 4-bit messages, andFIG.32here in4100illustrates the terms in the symbol-bit LLR conversion for 6-bit messages

InFIG.32, the values of each of the LLR terms L5bit,0, L5bit,1, L4bit,0, L4bit,1, . . . , L0bit,0, L0bit,1, are defined by the terms above them in the same column. For example, L4bit,0in column 4 is defined as L4bit,0LLR0symLLR1sym. . .LLR15symLLR32symLLR33sym. . .LLR47sym. The symboldenotes the maxstar operation that is defined in an earlier section for the OS-only scheme, where detailed explanation on the calculations of bit LLRs from symbol LLRs are provided. The bottom row3804inFIG.32defines the values of the aposteriori output bit LLRs, where in this OS-PSK scheme comprises of two sets of soft bits: the OS bit LLRs (MS QmLLRs: LLR5bit,outLLR4bit,out) and the PSK bit LLRs (LS k LLRs: LLR3bit,outdown to LLR0bit,out) The output OS bit LLRs may be referred to as the first set of aposteriori soft bits comprising k soft bits, and the output PSK bit LLRs as the second set of aposteriori soft bits comprising Qmsoft bits.

To obtain (as in step4514ofFIG.36B) the extrinsic bit LLRs904, as shown inFIG.15B, the feedback apriori bit LLRs910are subtracted from the aposteriori bit LLRs909. This relation with the bits corresponding to OS and PSK schemes separated from each other may be described as follows. The first set of aposteriori soft bits are combined with the soft bits of the second set of apriori soft signals, to obtain a first set of extrinsic soft bits comprising k soft bits. The second set of aposteriori soft bits are combined with the soft bits of the fourth set of apriori soft signals, to obtain a second set of extrinsic soft bits comprising Qmsoft bits.

Symbol-to-Symbol LLR Circuit

Now the question is how symbol LLRs1529are calculated (as in step4528ofFIG.36B) using the LLRs coming from the upstream908,1532and downstream feedback bit LLRs910. In the OS-only scheme, OS symbol LLRs908were a function of correlator output magnitudes109(as the first set of inputs to the soft demapper circuit1522) and feedback OS apriori feedback bit LLRs910(as the second set of inputs to the soft demapper circuit). Here, symbol LLRs are additionally a function of the correlator output phases1530as the third set of inputs, and feedback PSK apriori bit LLRs as the fourth set of inputs, to the soft demapping circuit1522. The feedback PSK bit LLRs can equivalently be referred to, in some examples, as the fourth set of apriori soft signals.

The Symbol-to-symbol LLR circuit1502takes as inputs the following to generate (as in step4528ofFIG.36B) symbol LLRs1529: OS symbol LLRs908, PSK symbol LLRs1532, and symbol LLRs1534that are converted (as in step4527ofFIG.36B) from downstream bit LLRs. The latter symbol LLRs1534may be referred to as the feedback symbol LLRs (shown inFIG.15B) which includes both OS and PSK apriori symbol LLRs as the second and fourth sets of apriori soft signals, respectively. Each of the M=16 OS symbol LLRs corresponds to one combination amongst M=16 of the k=4 LS bits of the 6-bit message, and 6 bits can represent 64 combinations. Given that the Qm=2 MS bits of the message can also have four combinations by themselves (‘00’, ‘01’, ‘10’, ‘11’), in some examplesFIG.28can be extended for the work in this section according toFIG.33, which illustrates in4200the 6-bit message combinations4201and corresponding LLR parameters3703,4202,4203.

InFIG.33, the OS symbol LLRs3703have the same values as the symbol LLRs3703in the earlier discussion for the OS-only scheme: LLRiOS,inOS-PSK=LLRisym,inos-onlyfor all i in the range [0,M−1] or [0,15]. This is because the work discussed earlier was based on an OS-only scheme where the cross-correlation data were just magnitudes, but not phases. As shown inFIG.33, the M=16 OS symbol LLR values3703are repeated after every M=16 combinations. Also, assume that each message combination (out of 2k+Qm=64) has its own PSK symbol LLR—it will be explained why this assumption holds and explain how the PSK symbol LLRs4202are calculated in step4525. Each message combination will also have a corresponding feedback symbol LLR4203too, as shown in the right-most column inFIG.33. The symbol LLR value for every message combination will be the addition of all LLR terms in the corresponding row inFIG.31. For example, LLR31sym=LLR15OS,in+LLR31PSK,in+LLR31sym,bit. In other words, all apriori soft signals90815321534are combined in the Symbol to Symbol LLRs circuit1502to generate in step4528a set of 2k+Qmaposteriori soft signals or symbol LLRs1529, which are combined 1533 in step4514to obtain the first set of aposteriori soft bit LLRs909and the second set of aposteriori soft bits.

Bit-Symbol LLR Circuit

The feedback bit LLRs910inFIG.15Amay contain LLR values fed back from the decoder912to the soft demapping circuit1522. If the decoder912is not feeding back any LLRs, the feedback bit LLRs910will have (as in step4512ofFIG.36B) bit LLR values of zero, making no difference in the computation (as in steps4527and4528) of symbol LLRs1529. Otherwise, the fed back bit LLR values affect (as in steps4519and4527) the computation of step4528inside the Convert to Bit LLRs circuit1521in an iterative decoding operation of step4521.FIG.34illustrates in4300the conversion of apriori bit LLRs into the symbol LLR domain which are represented by the feedback symbol LLR parameters LLR63sym,bit, LLR62sym,bit, . . . , LLR0sym,bit4203(the right-most column inFIG.33). An explanation of one example approach as to how the feedback symbol LLRs4301are calculated in step4527has been previously described.

Note that in some examples a constant value of (LLR5bit,in+LLR4bit,in+LLR3bit,in+LLR2bit,in+LLR1bit,in+LLR0bit,in)/2 may be added to all 64 rows inFIG.34, without affecting the extrinsic LLRs that are ultimately generated by the overall soft demapping circuit1522. Adding this constant has the advantage of eliminating all divisions by 2 and reducing the number of additions in the computations ofFIG.34, following some simplifications.

Conversion to PSK Symbol LLRs

It was assumed inFIG.33that each of the 2k+Qmmessage combinations4201has a corresponding PSK LLR. The PSK LLRs are generated (as in step4525ofFIG.36A) by the Convert to PSK symbol LLRs circuit1501as shown inFIG.15B. This conversion, for a 16OS-QPSK scheme, takes (as in step4509ofFIG.36A) as inputs M=16 correlator outputs1525for a (k+Qm) 6-bit message and generates 2k+Qm=64 PSK LLRs1532as outputs. From earlier, it was shown that the phases of the M=16 complex correlator outputs1525are meant to contain the Qm=2 MS bits of information of the transmitted 6-bit message. For example, consider the case where the true phase is −π/2 for a 6-bit message x. Now, it may be observed that in normal channel conditions, one correlator output (most probably the correct 1-Q pair), has a phase of −π/2, and the phase values of the remaining M−1=15 correlator outputs (most probably the incorrect I-Q pairs), will be uniformly distributed from the applicable range of [−π, π). Therefore, if the true phase −π/2 is subtracted from all the M=16 correlator output phases, at least one phase error is expected to be near zero, and the rest of the phases are expected to have random values in the above applicable range.

Note that while the QPSK phases [π/4, 3π/3, −π/4, −3π/4] were used in the datasets described earlier for the OS-only scheme, one example technique will be introduced that allows these 16OS-QPSK datasets to be generalised and used to model any PSK radix in the previous testbench discussion. For the sake of simplicity, phase mappings that begin with a phase of 0 will be adopted, such as [0, π/2, −π/2, −π] for QPSK, in this example embodiment.

On the other side, the distributions of correct and incorrect phase error values may be considered. The phase errors calculated above (resulting from the subtraction of an ‘assumed’ true phase from the M=16 received phases) may be applied to the probability density functions (PDFs) of both the distributions, in order to obtain the probabilities of each correlator output to be a) a correct correlator output or b) an incorrect correlator output. Using these conditional probabilities, the probability ratios (i.e., the PSK LLRs) may be calculated for each of the M=16 correlator outputs, according to the formulae below:

PSK⁢LLR=Δln[Pr(correct|corr⁢e⁢lator⁢output)Pr(incorrect|correlator⁢output)]

Applying Bayes theorem gives:

PSK⁢LLR=ln[Pr(correlator⁢output|correct)×Pr⁡(correct)Pr(correlator⁢output|incorrect)×Pr⁡(incorrect)]

Here, the conditional probabilities are characterised by the normal and the uniform distributions, and it may be assumed that Pr (correct)=1/M and Pr (incorrect)=(M−1)/M. Making these substitutions gives:

PSK⁢LLR=ln[N⁢o⁢r⁢m⁢alPDF⁡(phase⁢error,0,σc⁢o⁢r⁢r⁢e⁢c⁢t)0.5/π×(M-1)]

Here, 0.5/π is the value of the uniform distribution across the range −π to +π. Note that here ‘phase error’ refers to the difference between the phase of the corresponding correlator output and the phase of the corresponding one of the 2k+Qmmessage combinations.

In the above example, it was assumed that the true phase is −π/2; i.e., a fixed value was assumed for the 2 MS bits of the message. However, this assumption is made in the absence of any knowledge of what the true phase is, and hence, the above approach must be applied to all PSK phase possibilities (that is 4 phases in a QPSK scheme). The steps in the conversion1501(as in step4525ofFIG.36A) to PSK symbol LLRs1532can therefore be summarised into the following: assume one possible PSK phase value (e.g. one value in [0, π/2, −π/2, −π] for QPSK), subtract it from the M correlator phases1530, apply resulting phase error values to the correct and incorrect PDFs, calculate the PSK LLR for each correlator value from the PDF outputs, and repeat these steps from the beginning with a new PSK phase value. This process of step4525produces, in a 16OS-QPSK scheme, M.2Qmor 16×4=64 PSK symbol LLRs1532, which correspond to the input PSK LLR parameters4202inFIG.33shown as LLR63PSK,in, LLR62PSK,in, . . . , LLR0PSK,in. In summary, it can be said that the output phases1530of the M correlator outputs1525are combined with the second set of distribution parameters (σphase)1523of the aggregated correlator output phase distribution, in order to obtain in step4525a third set of apriori soft signals comprising 2k+Qmsoft phases (PSK symbol LLRs1532).

Summary

In summary, a soft demapping circuit1522has been proposed for performing soft-decision demodulation in the receiver chain1510of a transmission system1500comprising a transmitter chain1503, a channel1536and a receiver chain1510. The transmitter chain1503signals a first set of bits1508comprising k bits by transmitting a spreading sequence1511that is selected from a set of 2kpossible signals according to the values of the k bits, and the transmitter signals a second set of bits1509comprising am bits by rotating the phase of the spreading sequence1511using a rotation selected from a set of 2Qmpossible rotations according to the values of the Qmbits in the modulator circuit1504. The receiver chain1510uses a bank of 2kcorrelators to detect the transmission of each possible signal, and wherein the 2kmagnitudes1531of the correlator outputs1525are provided as a first set of inputs to the soft demapping circuit1522, and wherein a second set of apriori soft signals or feedback bit LLRs910comprising k soft bits are provided as a second set of inputs to the soft demapping circuit1522. Furthermore, the 2kphases1530of the correlator outputs1525are provided as a third set of inputs to the soft demapping circuit1522, and a fourth set of apriori soft signals or feedback bit LLRs910comprising Qmsoft bits are provided as a fourth set of inputs to the soft demapping circuit1522. The soft demapping circuit1522calculates a first set of aposteriori soft bits comprising k soft bits based on statistics and parameters1520derived from a plurality of aggregated correlator output magnitude distributions, and wherein the soft demapping circuit1522calculates a second set of aposteriori soft bits comprising Qmsoft bits based on statistics (the σphaseparameter1523) derived from a plurality of aggregated correlator output phase distributions. Furthermore, the soft demapping circuit1522combines (as in step4528ofFIG.36B) all apriori soft signals90815321534to generate a set of 2k+Qmaposteriori soft signals or symbol LLRs1529and wherein the set of 2k+Qmaposteriori soft signals or symbol LLRs1529are combined (as in step4513ofFIG.36B) to obtain the first set of aposteriori soft bit LLRs909and the second set of aposteriori soft bits. Following that, the first set of aposteriori soft bits are combined with the soft bits of the second set of apriori soft signals, in order to obtain a first set of extrinsic soft bits. Finally, the second set of aposteriori soft bits are combined with the soft bits of the fourth set of apriori soft signals, in order to obtain a second set of extrinsic soft bits comprising Qmsoft bits.

The operation4500of the example soft demapper for the OS-PSK scheme, as demonstrated in the flowchart ofFIG.36, may be summarised as follows. Initially at4501, the demapper receives4502a set of signals as the outputs of the demodulator that correspond to a transmitted frame. Then, it is decided at4503and at4504which of the three different methods of estimating the distributions of the correlator output magnitudes and phases, are to be employed inFIG.36A. If the offline approach of, say, step4523is chosen, the characteristics of the channel, such as the signal-to-noise ratio and channel type (e.g. AWGN), are identified in step4505. The result of the identification is then used to address a pre-computed look up table1527of parameters for the distributions of the correlator output magnitudes and phases. If, in contrast, an online approach of distribution estimation of step4524is chosen, the choice will be either the use of synchronisation sequences (as in step4507ofFIG.36A), or applying the largest magnitude of step4508. Whichever estimation approach is employed, the estimated parameters of the magnitude distributions, alongside the collected correlator output magnitudes of step4509ofFIG.36A, are used to calculate (as in step4510ofFIG.36A) the OS soft signals, which may be in the form of LLRs, as described previously. Similarly, the calculation in step4525of the PSK soft signals is performed using the correlator output phase values and the estimated parameters of the phase error distribution, as described in the example embodiment for conversion to PSK symbol LLRs.

Following that, the calculated OS (as in step4510ofFIG.36A) and PSK (as in step4525) soft signals are stored (as in steps4511and4526) in internal memory, in case a demap-decode round of iterative decoding is required later. Since, only the first iteration has been started so far, of a possible iterative decoding process, there is no value on the feedback path from the decoder. Hence, this feedback will be all zero-valued, as in step4512ofFIG.36B. The feedback path—albeit zero valued as of now—is in the LLR domain (soft bits), and hence is converted in step4527into the symbols domain to provide the feedback apriori soft signals (as previously described with respect to the Bit-symbol LLR circuit of a soft demapper). The OS and PSK soft signals, which were previously calculated in steps4510and4525, are also of the apriori type, and combined with the feedback apriori soft signals, are used to calculate in step4528OS-PSK soft signals (as previously described with respect to the Symbol-to-symbol LLR circuit of a soft demapper). These signals are aposteriori information as they are a function of the feedback path, despite so far being zero valued.

Next, the aposteriori OS-PSK symbols are converted in step4513into the LLRs domain to give the aposteriori soft bits (as previously described with respect to the Symbol-bit-LLR circuit). This is followed by converting in step4514the aposteriori soft bits into extrinsic soft bits as the type required by, and sent in step4515to, the decoder. After the decoder is finished with the process of decoding the extrinsic information it received, the demapper receives in step4516the CRC status and the decoded soft bits from the decoder. Following a check in step4517of the CRC status, if it is failed in step4521, turbo decoding is applied and the decoded soft bits received in step4516are treated in step4519as apriori information fed back from the downstream decoder. Also, the OS and PSK soft signals, which had been calculated previously in steps4510and4525, are loaded in steps4518and4529from memory as another set of apriori information for the second demapping iteration. The fed back apriori soft bits are converted in step4527into the soft signals domain, and the soft demapper uses all apriori information to make a second calculation (as in step4528ofFIG.36B) of aposteriori soft signals. The demap-decode iterations of turbo decoding are ended in step4522either by a CRC pass or abandoning further decodes of the current frame, which may lead into, for example, a HARQ retransmission. At this point, the demapping of the current frame is ended in step4520.

Testbench

Here, the testbench evaluates a soft demapping circuit1522, for the OS-PSK scheme, and the testbench is demonstrated in the block diagram ofFIG.18AandFIG.18B, which illustrates the 16OS-PSK testbench using 16OS-QPSK datasets. For a given dataset, the testbench1800generates three extrinsic information transfer (EXIT) charts1801on different parts of the message bits/LLRs: namely the OS bits/LLRs of the OS flow1802, the PSK bits/LLRs of the PSK flow1803, and the combined OS-PSK bits/LLRs of the combined flow1804(i.e., the entire encoded/decoded message). Parts of the testbench1800that are in the set113,114,904,909,1001,1501,1506,1507,1508,1509,1511,1515,1517,1521,1522,1524,1526,1530,1531,1532,1808,1811,1814,1815inFIG.18AandFIG.18Brepresent the flow1805, which is present in real transmission systems1500, and the remaining parts are the flows1806exclusive to the testbench. The testbench1800inFIG.18AandFIG.18Bhas the same operation as the testbench1000demonstrated inFIG.10.

As the datasets used in this current work's testbench1800are based on OS-PSK schemes, that is different from the earlier discussed OS-only models and datasets1012, the datasets1807and the functionality of the Simulate channel circuit1900inFIG.18AandFIG.18Bare different from the corresponding circuits10121021in the OS-only scheme. Example OS-PSK datasets1807have been described and introduced previously, and the Simulate channel circuit1900described below. The above-listed parts inFIG.18AandFIG.18Bshare the same circuits with the block diagram ofFIG.15AandFIG.15B, except that the hard demapper circuit1808is added (as it was for the OS-only scheme) in this testbench1800so that the bit error rates (BER)1809of the hard decision demapping flow1812is compared against those1810of the soft decision demapping flow1813.

Channel Simulation

In the example testbench1800, a given channel is simulated in the simulate channel circuit1900using its corresponding dataset, from the set of datasets1807presented earlier, which all belong to the 16OS-QPSK scheme. In some examples, the testbench1800may be arranged to simulate other PSK schemes; such as BPSK, 8PSK or 16PSK. For this, it is necessary to remove, from the datasets1807, the dependence on the QPSK scheme. This can be performed by the method described for the OS-only scheme: for every pair of ‘1 16OS-QPSK symbol-16 correlator I-Q pairs’, the true phase embedded in the QPSK part of the 16OS-QPSK symbol value is subtracted from the phase of each correlator complex value (as the received QPSK phase). This is shown inFIG.19, which illustrates the 16OS-nPSK channel simulation using 16OS-QPSK dataset. To find out the true phase value, the result of dividing1901the 16OS-QPSK symbol value by M=16 (with rounding1902towards zero) is used to choose the phase in the QPSK phase mapping circuit1903. The testbench1800is generic with respect to the OS modulation order and can have any value of M=2k.

Given that the 16OS-QPSK symbol values4001are in the range [0, 2k+Qm−1] or [0,63](FIG.31), the division1901by M=16 (followed by rounding1902) will give a value in the range [0,2Qm-1] or [0,3], that is equal to the Qm=2 MS bits of 16OS-QPSK symbol's binary value. Subtraction of phases is performed by dividing1904the complex number with the received phase by a complex number of magnitude one1905and the true phase value, as shown by the ‘ ’ operator inFIG.19. With this computation, the 16OS-QPSK dataset is transformed into a new set of data1906with 16OS symbols and PSK phase errors: the 16OS-PSK data. This transformation is performed offline1909, shown by dotted lines inFIG.19. The flow during the simulation1910is shown by solid lines inFIG.19.

The new 16OS-PSK data1906can be used to simulate 16OS-PSK schemes with any degrees of PSK modulation; such as BSPK, 8PSK, or nPSK (n=2Qm) in the generic case. From the inputs to the circuit inFIG.19, k=4 bits define the 16OS symbol value, and one row from the 16OS-PSK data1906with the same input 16OS symbol value is randomly chosen. The M=16 complex I-Q pairs in the corresponding row of the data are multiplied1907by a complex number with a magnitude of one and a phase that is defined by the other input to the simulate channel circuit1900: the Qmbits of an nPSK scheme (where n=2Qm). This multiplication1907inverses the effect of the division1904performed offline previously to calculate the phase errors: the multiplication1907adds to the phase errors the phase from the nPSK scheme. The result is M=16 correlator I-Q pairs that become the output1811for the simulate channel circuit1900.

Note that the phase mappings [π/4, 3π/4, −π/4, −3π/4] are always adopted in the QPSK phase mapping circuit1903inFIG.19, in accordance with the datasets described earlier in this section. By contrast, the nPSK phase mapping circuit1908inFIG.19adopts different phases depending on which PSK modulation order is being simulated. For the sake of simplicity, a phase mapping that begins with a phase of ‘0’ is adopted. In the case of QPSK, the phase mapping [0, π/2, −π/2, −π] is adopted here, which preserves the Gray mapping, but with a rotation of π/4 radians with respect to the datasets1807mentioned above. For BPSK [0, −π] is adopted, for 8PSK [0 1 3 2 −1 −2 −4 −3]*π/4 is adopted, for 16PSK [0 1 3 2 7 6 4 5 −1 −2 −4 −3 −8 −7 −5 −6]*π/8 is adopted and for 32PSK [0 1 3 2 7 6 4 5 15 14 12 13 8 9 11 10 −1 −2 −4 −3 −8 −7 −5 −6 −16 −15 −13 −14 −9 −10 −12 −11]*π/16 is adopted, which all implement Gray mapping.

PSK Hard Demapping

It was mentioned earlier that a hard decision demapping flow1812is used in the testbench1800to compare the hard BERs1809with the soft BERs1810in the EXIT charts1801. The k=4 bits of the message modulated by the OS scheme are demapped by the hard demapper circuit1808in the hard decision for symbol circuit1814by taking the index of the largest-magnitude I-Q complex pair and converting it to binary in the convert to bits circuit1815. This is described in full detail above, for the OS-only scheme.

To find out Qmbits of the message modulated by the PSK scheme, the Hard Demapper circuit1808(FIG.18B) applies the following approach. The hard decision for symbol circuit1814first calculates the difference between the phase of the largest-magnitude correlator I-Q pair (amongst the received M=16 correlator outputs) with each phase in the phase mapping vector (e.g., vector [0, π/2, −π/2, −π] in QPSK) in the applied PSK scheme. The hard decision for symbol circuit1814of the hard demapper circuit1808then takes the PSK phase that has the least difference with the phase of the largest-magnitude I-Q complex value as the phase of the PSK symbol and converts the index of this phase to binary according to the PSK's phase mapping vector in the convert-to-bits circuit1815. For example, it may be assumed that a QPSK modulation with a mapping vector [0, π/2, −π/2, −π] is applied, and the phase of the largest-magnitude I-Q pair is 5π/3. With some calculations, it may be observed that the angle 5π/3 in the [0, 2π] range is closest to the angle −π/2 in the [−π, π] range (of the phase mapping). The value −π/2 has an index of 2 in QPSK mapping [0, π/2, −π/2, −π], and, hence, the hard demapper circuit1808binary value for the Qm=2 MS bits of the 16OS-QPSK modulation in this example will be ‘10’.

Per-Scheme Quality Measurements

With the 16OS-PSK schemes that was simulated using the example testbench1800, it is possible to measure the quality parameters, not only of the results representing all message bits in the combined flow1804, but also on parts of the message corresponding to each of the OS flow1802and the PSK flow1803. For example, in a 16OS-QPSK scheme with N bits in a code block in total, every k+Qm=6 bits of the encoded bits1001of the transmitter and on the hard demapper's output bits1035will comprise of k=4 16OS bits, Qm=2 QPSK bits, and the total 6 16OS-QPSK bits—each of the three referred to as a bit group. The same can be said, in terms of LLR groups, for the bit LLRs generated from the soft demapper circuit1522, which includes both the extrinsic bit LLRs904and aposteriori bit LLRs1042. The bit and LLR groups, when processed simultaneously, are shown by three cascaded arrows, indicating the OS flow1802, PSK flow1803and combined flow1804inFIG.16.

FIG.20AandFIG.20Bdemonstrate three EXIT charts2000for the three different 16OS-QPSK modulation schemes. The plot2001inFIG.20Acorresponds to the Qm=2 QPSK bits/bit LLRs of the first 6-bit transmission made during the processing of a set of N bits, and to the Qm=2 QPSK bits/bit LLRs of the second transmission, and so on until the Qm=2 QPSK bits/bit LLRs of the last transmission made during the processing of the N bits. The same can be said for plot2002ofFIG.20B: the plot data corresponds to the set of all k=4 16OS bits/bit LLRs where each appear in a different transmission, amongst all transmissions, for N bits.

Measuring at1036the hard BERs1809for the 16OS-PSK, 16OS, and PSK flows requires encoded bits groups1001from the transmitter and the hard demapper's output bits1035, as shown inFIG.18A. The three soft BER values1810are computed at1005from the encoded bits groups1001of the transmitter chain (as the reference) and the aposteriori bit LLR groups1042that are turned at1041into hard bits1816(as the soft-then-hard bits). Measuring at1006the mutual information histogram (MI) values of the bit LLRs for the three modulation schemes in a message is made possible by using the encoded bits groups1001from the transmitter chain and the extrinsic bit LLR groups904from the soft demapping circuit1522. These extrinsic bit LLR groups904are also used to calculate1007the MI averaging values, as shown inFIG.18A.

Results

FIG.25demonstrates the EXIT charts for the MP_-7.5 dB dataset for different PSK modulation orders of QPSK2501, 8PSK2502, 16PSK2503, and 32PSK2504. Only the EXIT charts2500for the worst-case SNR value of −7.5 dB were chosen for inclusion as higher SNRs resulted in all quality measures (including MI values and the 1-BER figure) to be close to 1. Also, there was not much difference between the results from the AWGN and MP channels, so only the multipath channel charts2500were included. Details of what information is included in the EXIT charts are described above for the OS-only scheme.

EXIT Charts

OS Charts—PSK Charts

From the EXIT charts2500, it may be observed that increasing the PSK modulation order, except for small changes, does not have much effect on the quality of the 16OS LLRs characterised in the EXIT charts2505. This means that the model underlying the datasets1807could, to a great extent, keep the modulation schemes independent from each other. In contrast, increasing the PSK modulation order does have noticeable effect on the quality of PSK bit LLRs characterised in the EXIT charts2506: the quality measures decrease with increasing PSK modulation order. This is expected, as the increase in the number of modulated phases increases the chances of error in both (i) making the precise phases in transmitted signals, and (ii) in identifying the correct phase from the more error-prone data.

It is worth noting that in the specific case of 16OS-QPSK, the OS bit LLRs and the PSK bit LLRs have similar quality to each other. In the higher order PSK schemes, the PSK bit LLRs have lower quality than the OS bit LLRs.

OS-PSK Charts

The EXIT charts2507for the OS-PSK symbols are based on measurements on all bit LLRs of the combined flow1804(including both OS bit LLRs of the OS flow1802and PSK bit LLRs of the PSK flow1803), and hence demonstrate an average behaviour between the corresponding OS EXIT charts2505and PSK EXIT charts2506for each PSK modulation order. For example, for the 16OS-32PSK scheme at MI=0, while the histogram MI measurements for the 16OS bit LLRs and 32PSK bit LLRs are 0.87 and 0.44, respectively, the 16OS-32PSK bit LLRs have a histogram MI value of 0.63, that is a value between the other two measurements. Here, the average is a weighted average—in the case of 16OS-32PSK, the OS bit LLRs carry only 4/5 of the weight of the PSK bit LLRs.

In the discussion of the OS-only scheme, the EXIT charts1300and1400were generated1000from a single scheme (16OS) and the upper bounds on the coding rate were represented by the area underneath the MI histogram plot. In this section, there are schemes with different PSK modulation orders, and hence different total number of transmitted bits for the schemes are present. For example, the 16OS-QPSK scheme of the EXIT chart2501contains 4+2=6 bits per transmission, and the 16OS-8PSK scheme of the EXIT chart2502is based on 4+3=7 bits per transmission. However, while the number of encoded bits per transmission increases with increasing PSK modulation order, the reliability of the transmission decreases, and a lower channel coding rate is needed. It may be expected that a ‘sweet spot’ can be found where the benefit of increasing the number of encoded bits per transmission maximally outweighs the cost of requiring reduced coding rate. Given that the coding rate is a function of the total number of transmitted bits, in each 16OS-nPSK EXIT chart2500the area below the histogram MI plot is multiplied by the total number of bits in order to provide a common measure of information transfer that is comparable across different nPSK schemes with different n. For example, in moving from the 16OS-QPSK EXIT chart2501to the 16OS-8PSK EXIT chart2502, although the MI plots decrease in value, but it may be observed that the upper bound2508in the achievable coding rate (for both with and without iterative feedback) increases. This is because, although the area below the histogram MI plot decreases in this movement, the increase in the number of transmitted bits from 6 to 7 causes a considerable jump in the maximum coding rate, from 5.71 to 6.26 in the iterative case, for instance.

In general, it can be said that using higher order PSK allows more encoded bits per symbol, but imposes a lower channel coding rate in order to achieve reliable decoding—these two effects act against each other, but to different degrees at different PSK modulation orders. The results suggest that the 16OS-16PSK scheme of the EXIT chart2503, with maximum achievable coding rates of 6.31 and 5.79 for with and without iterative feedback, respectively, can convey the most information. However, phase tracking can be expected to become more challenging for this high PSK modulation order, which might favour a reduced value in practice. Also, the 4 PSK bits are more error prone than the 4 OS bits, which might make the channel decoder optimisation more difficult. By contrast, it may be observed that in the 16OS-QPSK scheme of the EXIT chart2501, the 2 PSK bits and the 4 OS bits are equally error prone (more or less), and support reliable phase tracking, which might be expected to yield a reduced implementation challenge.

One Example Application

Referring now toFIG.37, there is illustrated a typical computing system4600that may be employed to implement soft demapping according to some example embodiments of the invention. Computing systems of this type may be used in wireless communication units. Those skilled in the relevant art will also recognize how to implement the invention using other computer systems or architectures. Computing system4600may represent, for example, a desktop, laptop or notebook computer, hand-held computing device (PDA, cell phone, palmtop, etc.), mainframe, server, client, or any other type of special or general purpose computing device as may be desirable or appropriate for a given application or environment. Computing system4600can include at least one processors, such as a processor4604. Processor4604can be implemented using a general or special-purpose processing engine such as, for example, a microprocessor, microcontroller or other control logic. In this example, processor4604is connected to a bus4602or other communications medium. In some examples, computing system4600may be a non-transitory tangible computer program product comprising executable code stored therein for implementing soft demapping.

Computing system4600can also include a main memory4608, such as random access memory (RAM) or other dynamic memory, for storing information and instructions to be executed by processor4604. Main memory4608also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor4604. Computing system4600may likewise include a read only memory (ROM) or other static storage device coupled to bus4602for storing static information and instructions for processor4604.

The computing system4600may also include information storage system4610, which may include, for example, a media drive4612and a removable storage interface4620. The media drive4612may include a drive or other mechanism to support fixed or removable storage media, such as a hard disk drive, a floppy disk drive, a magnetic tape drive, an optical disk drive, a compact disc (CD) or digital video drive (DVD) read or write drive (R or RW), or other removable or fixed media drive. Storage media4618may include, for example, a hard disk, floppy disk, magnetic tape, optical disk, CD or DVD, or other fixed or removable medium that is read by and written to by media drive4612. As these examples illustrate, the storage media4618may include a computer-readable storage medium having particular computer software or data stored therein.

In alternative embodiments, information storage system4610may include other similar components for allowing computer programs or other instructions or data to be loaded into computing system4600. Such components may include, for example, a removable storage unit4622and an interface4620, such as a program cartridge and cartridge interface, a removable memory (for example, a flash memory or other removable memory module) and memory slot, and other removable storage units4622and interfaces4620that allow software and data to be transferred from the removable storage unit4618to computing system4600.

Computing system4600can also include a communications interface4624. Communications interface4624can be used to allow software and data to be transferred between computing system4600and external devices. Examples of communications interface4624can include a modem, a network interface (such as an Ethernet or other NIC card), a communications port (such as for example, a universal serial bus (USB) port), a PCMCIA slot and card, etc. Software and data transferred via communications interface4624are in the form of signals which can be electronic, electromagnetic, and optical or other signals capable of being received by communications interface4624. These signals are provided to communications interface4624via a channel4628. This channel4628may carry signals and may be implemented using a wireless medium, wire or cable, fibre optics, or other communications medium. Some examples of a channel include a phone line, a cellular phone link, an RF link, a network interface, a local or wide area network, and other communications channels.

In this document, the terms ‘computer program product’, ‘computer-readable medium’ and the like may be used generally to refer to media such as, for example, memory4608, storage device4618, or storage unit4622. These and other forms of computer-readable media may store at least one instruction for use by processor4604, to cause the processor to perform specified operations. Such instructions, generally referred to as ‘computer program code’ (which may be grouped in the form of computer programs or other groupings), when executed, enable the computing system4600to perform functions of embodiments of the present invention. Note that the code may directly cause the processor to perform specified operations, be compiled to do so, and/or be combined with other software, hardware, and/or firmware elements (e.g., libraries for performing standard functions) to do so.

In an embodiment where the elements are implemented using software, the software may be stored in a computer-readable medium and loaded into computing system4600using, for example, removable storage drive4622, drive4612or communications interface4624. The control logic (in this example, software instructions or computer program code), when executed by the processor4604, causes the processor4604to perform the functions of the invention as described herein.

In the foregoing specification, the invention has been described with reference to specific examples of embodiments of the invention. It will, however, be evident that various modifications and changes may be made therein without departing from the scope of the invention as set forth in the appended claims and that the claims are not limited to the specific examples described above.

The connections as discussed herein may be any type of connection suitable to transfer signals from or to the respective nodes, units or devices, for example via intermediate devices. Accordingly, unless implied or stated otherwise, the connections may for example be direct connections or indirect connections. The connections may be illustrated or described in reference to being a single connection, a plurality of connections, unidirectional connections, or bidirectional connections. However, different embodiments may vary the implementation of the connections. For example, separate unidirectional connections may be used rather than bidirectional connections and vice versa. Also, plurality of connections may be replaced with a single connection that transfers multiple signals serially or in a time multiplexed manner. Likewise, single connections carrying multiple signals may be separated out into various different connections carrying subsets of these signals. Therefore, many options exist for transferring signals.

Those skilled in the art will recognize that the architectures depicted herein are merely exemplary, and that in fact many other architectures can be implemented which achieve the same functionality.

Any arrangement of components to achieve the same functionality is effectively ‘associated’ such that the desired functionality is achieved. Hence, any two components herein combined to achieve a particular functionality can be seen as ‘associated with’ each other such that the desired functionality is achieved, irrespective of architectures or intermediary components. Likewise, any two components so associated can also be viewed as being ‘operably connected,’ or ‘operably coupled,’ to each other to achieve the desired functionality.

Furthermore, those skilled in the art will recognize that boundaries between the above-described operations merely illustrative. The multiple operations may be combined into a single operation, a single operation may be distributed in additional operations and operations may be executed at least partially overlapping in time. Moreover, alternative embodiments may include multiple instances of a particular operation, and the order of operations may be altered in various other embodiments.

The present invention is herein described with reference to an integrated circuit device comprising, say, a microprocessor configured to perform the functionality of the soft demapper. However, it will be appreciated that the present invention is not limited to such integrated circuit devices, and may equally be applied to integrated circuit devices comprising any alternative type of operational functionality. Examples of such integrated circuit device comprising alternative types of operational functionality may include, by way of example only, application-specific integrated circuit (ASIC) devices, field-programmable gate array (FPGA) devices, or integrated with other components, etc. Furthermore, because the illustrated embodiments of the present invention may for the most part, be implemented using electronic components and circuits known to those skilled in the art, details have not been explained in any greater extent than that considered necessary, for the understanding and appreciation of the underlying concepts of the present invention and in order not to obfuscate or distract from the teachings of the present invention. Alternatively, the circuit and/or component examples may be implemented as any number of separate integrated circuits or separate devices interconnected with each other in a suitable manner.

Also, for example, the examples, or portions thereof, may implemented as soft or code representations of physical circuitry or of logical representations convertible into physical circuitry, such as in a hardware description language of any appropriate type.

Also, embodiments of the invention are not limited to physical devices or units implemented in non-programmable hardware but can also be applied in programmable devices or units able to perform the desired soft demapping by operating in accordance with suitable program code, such as minicomputers, personal computers, notepads, personal digital assistants, electronic games, automotive and other embedded systems, cell phones and various other wireless devices, commonly denoted in this application as ‘computer systems’.

However, other modifications, variations and alternatives are also possible. The specifications and drawings are, accordingly, to be regarded in an illustrative rather than in a restrictive sense.

In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word ‘comprising’ does not exclude the presence of other elements or steps then those listed in a claim. Furthermore, the terms ‘a’ or ‘an,’ as used herein, are defined as at least one than one. Also, the use of introductory phrases such as ‘at least one’ and ‘at least one’ in the claims should not be construed to imply that the introduction of another claim element by the indefinite articles ‘a’ or ‘an’ limits any particular claim containing such introduced claim element to inventions containing only one such element, even when the same claim includes the introductory phrases ‘at least one’ or ‘at least one’ and indefinite articles such as ‘a’ or ‘an.’ The same holds true for the use of definite articles. Unless stated otherwise, terms such as ‘first’ and ‘second’ are used to arbitrarily distinguish between the elements such terms describe. Thus, these terms are not necessarily intended to indicate temporal or other prioritization of such elements. The mere fact that certain measures are recited in mutually different claims does not indicate that a combination of these measures cannot be used to advantage. The word ‘subset’ refers to a selection of elements from a set, where that selection may comprise one, some or all of the elements in the set.

REFERENCES

[1]J. Hagenauer, “The exit chart—introduction to extrinsic information transfer in iterative processing,” 2004 12th European Signal Processing Conference, Vienna, 2004, pp. 1541-1548.[2]3GPP TS 38.212, “NR; Multiplexing and channel coding”, v16.1.0, March 2020.[3]Z. B. Kaykac Egilmez, L. Xiang, R. G. Maunder and L. Hanzo, “The Development, Operation and Performance of the 5G Polar Codes,” in IEEE Communications Surveys & Tutorials, vol. 22, no. 1, pp. 96-122, First quarter 2020.[4]S. B. Wicker and V. K. Bhargava, eds. “Reed-Solomon codes and their applications,” John Wiley & Sons, 1999.[5]J. Neasham, “Simulated cross-correlation dataset for the 16-ary Orthogonal Signalling scheme,” Newcastle University, Oct. 1, 2020.[6]J. Neasham, “Simulated cross-correlation dataset for the 16-ary Orthogonal Signalling-Phase Shift Keying scheme,” Newcastle University, Oct. 1, 2020.[7]“Modulation using the 16-ary Orthogonal Signalling scheme, and the 16-ary Orthogonal Signalling-Phase Shift Keying scheme,” Newcastle University and Sonardyne International Ltd, 2020.