Patent Description:
In digital communication systems, clock and data recovery (CDR) are two inevitably blocks enabling data reconstruction. Equalizers are often implemented on both transmitter and receiver sides. They can be analog or digital. Analog equalizers perform partial data reconstruction and the final equalization is mostly done digitally by using digital signal processing algorithms (DSP). Linear and nonlinear imperfections can be efficiently compensated by a feedforward equalizer (FFE), a decision feedback equalizer (DFE), and a maximum likelihood sequence estimator (MLSE). The feedforward equalizer (FFE) has the lowest complexity while the maximum likelihood sequence estimator is the most complex algorithm. The complexity dictates performance so that the maximum likelihood sequence estimator (MLSE) is the best reconstruction method, the decision feedback equalizer (DFE) is better than the feedforward equalizer (FFE), and the feedforward equalizer (FFE) provides the worst performance.

In order to have a medium complexity but still good performance, the above algorithms can be combined. For example, the feedforward equalizer (FFE) is often combined with the decision feedback equalizer (DFE). In literature DFE stands for the combination of the feedforward equalizer (FFE) and the decision feedback equalizer (DFE).

In some applications, the feedforward equalizer (FFE) is used to decrease the memory in the received signal and the output from the feedforward equalizer (FFE) is further processed by the maximum likelihood sequence estimator (MLSE). This combination is often used in partial response signaling applications. For example, the feedforward equalizer (FFE) may target a partial response <NUM>+z-<NUM> and the maximum likelihood sequence estimator (MLSE) will process a signal with one symbol memory. The maximum likelihood sequence estimator (MLSE) may improve the performance of the feedforward equalizer (FFE) several times, and this gain depends on the number of modulation levels.

Also, the transmitter can be accommodated to the channel characteristic. The Tomlinson-Harashima precoding (THP) uses the knowledge about the system transfer function and conducts the precoding to improve performance. In this case, the decision feedback equalizer (DFE) is not necessary at the receiver side.

However, state-of-the-art equalizers either provide proper performance on signal processing but keep the latency and complexity of the processing high or reduce the complexity and latency of the processing but deliver poor performance on noise cancellation of a signal.

In light of the above, there is still a need for an improved device and a corresponding method for cancelling noise in a communication system more efficiently and effectively, especially achieving low latency and complexity.

<CIT> describes common mode noise cancellation.

The present disclosure seeks to provide an improved device and a corresponding method for cancelling noise in a communication system more efficiently and effectively.

The foregoing and other objects of the present disclosure are achieved by the subject matter of the independent claims.

According to a first aspect, the present disclosure relates to a processing device for cancelling noise in a communication system. More specifically, the processing device is a "noise canceller" for processing a plurality of input samples being affected by noise and cancelling the noise for these plurality of input samples.

In an embodiment, the plurality of input samples comprises at least a first input sample and a second input sample. In another embodiment, the plurality of input samples comprises more than two samples such as <NUM> input samples.

In an embodiment, the noise canceller comprises a noise cancellation stage which is configured to: buffer the first input sample and the second input sample; determine a first decision sample representing the first input sample and a second decision sample representing the second input sample; determine a first noise sample from a difference between the first input sample and the first decision sample; determine a second noise sample from a difference between the second input sample and the second decision sample; determine a sign pattern comprising a sign of the first decision sample and a sign of the second decision sample; process the first noise sample and the second noise sample in dependence on the sign pattern, to obtain a first processed noise sample and a second processed noise sample; and superimpose the first processed noise sample and the second processed noise sample to obtain a noise-cancelled sample.

The plurality of samples further comprises a third input sample, and the noise cancellation stage is further configured to determine the sign pattern comprising the sign of the first decision sample, the sign of the second decision sample and the sign of the third input sample.

Thus, an improved device is provided as a noise canceller, allowing for cancelling noise for input samples and delivering a noise-cancelled sample more efficiently and effectively.

The noise canceller is configured to superimpose the first processed noise sample, the second processed noise sample and the third input sample or a third decision sample representing the third input sample to obtain a noise-cancelled sample. Thus, a noise-cancelled sample is provided more efficiently with low latency and low complexity.

In a further possible implementation form of the first aspect, the noise canceller is configured to select a predetermined processing scheme from a plurality of processing schemes upon the basis of the sign pattern in order to process the first noise sample and the second noise sample.

Thus, an improved processing scheme can be provided for processing the noise samples.

Thus, aspects of the disclosed embodiments not only improve the bit error rate (BER) in the equalized systems by a novel noise prediction method but also improve decisions in feedforward manner without using any feedback connection.

Further, aspects of the disclosed embodiments achieve low latency as well as complexity saving and do not require overhead or precoding that is common in decision feedback equalizers (DFE).

The noise canceller according to the inventive embodiment is further configured to determine weighting parameters in dependence on the sign pattern, and to weight the first noise sample and the second noise sample with the respectively determined weighting parameter.

Thus, improved weighting parameters can be provided for weighting the noise samples.

In a further possible implementation form of the first aspect, the noise canceller is configured to multiply the first noise sample by a first weighting parameter and to multiply the second noise sample by a second weighting parameter or to shift binary values of the first noise sample and binary values of the second noise sample by a number of binary positions in order to weight the first noise sample and the second noise sample.

In a further possible implementation form of the first aspect, the noise canceller is configured to select predetermined weighting parameters from a predefined parameter set in dependence on the sign pattern, and to weight the first noise sample and the second noise sample with the selected predetermined noise parameters.

In a further possible implementation form of the first aspect, the noise canceller is configured to weight the first noise sample and the second noise sample by the same predetermined weighting parameter or by different weighting parameters if the sign pattern indicates that the signs of the first noise sample and the second noise sample are equal.

In a further possible implementation form of the first aspect, the noise canceller is configured to compare an absolute value of the first noise sample to an absolute value of the second noise sample, to determine a first weighting parameter and a second weighting parameter such that the first weighting parameter is equal to or greater than the first weighting parameter if the absolute value of the first noise sample is smaller than the absolute value of the second noise sample, to weight the first noise sample with the first weighting parameter, and to weight the second noise sample with the second weighting parameter.

In a further possible implementation form of the first aspect, the noise canceller is configured to zero or to discard the first noise sample and/or the second noise sample in order to output the third input signal sample as the noise-cancelled signal sample if the sign pattern indicates different signs of the first noise sample and the second noise sample.

Thus, an improved noise-cancelled sample is provided with low latency and low complexity.

In a further possible implementation form of the first aspect, the noise canceller comprises an input buffer which is configured to buffer the input signal samples.

Thus, an improved input buffer is provided for buffering input signal samples efficiently.

In a further possible implementation form of the first aspect, the noise canceller comprises one or more signal slicers configured to provide the decision samples, in particular hard-decided decision samples,.

Thus, one or more improved signal slicers are provided for generating decision samples efficiently.

In a further possible implementation form of the first aspect, the noise canceller comprises one or more subtractors to determine the first noise sample and the second noise sample.

Thus, one or more improved subtractors are provided for determining noise samples efficiently.

In a further possible implementation form of the first aspect, the noise canceller comprises a superimposer, in particular an adder, for superimposing the first processed noise sample and the second processed noise sample.

Thus, an improved superimpose is provided for superimposing processed noise samples efficiently.

In a further possible implementation form of the first aspect, the noise canceller comprises one or more multipliers for weighting the first noise sample and the second noise sample with weighting parameters.

Thus, one or more improved multipliers are provided for weighting noise samples with weighting parameters efficiently.

In a further possible implementation form of the first aspect, the noise canceller comprises an error analyzer, the error analyzer being configured to determine a processing scheme for processing the noise samples in dependence on the sign pattern, and the error analyzer is further configured to output weighting parameters according to the determined processing scheme for weighting the noise samples.

Thus, an improved error analyzer is provided, allowing for determining an improved processing scheme for processing the noise samples and outputting improved weighting parameters for weighting the noise samples.

In a further possible implementation form of the first aspect, the noise canceller further comprises a subsequent noise cancellation stage. In an embodiment, the subsequent noise cancellation stage is configured to receive noise-cancelled signal samples from the noise cancellation stage, the received noise-cancelled signal samples forming a first input sample and a second input sample for sample processing in the subsequent noise cancellation stage.

In a further embodiment, the subsequent noise cancellation stage is further configured to determine a first decision sample representing the first input sample and a second decision sample representing the second input sample; determine a first noise sample from a difference between the first input sample and the first decision sample, determine a second noise sample from a difference between the second input sample and the second decision sample; determine a sign pattern comprising signs of the first decision sample and the third decision sample; process the first noise sample and the second noise sample in dependence on the sign pattern to obtain a first processed noise sample and a second processed noise sample; and superimpose the first processed noise sample and the second processed noise sample to obtain a noise-cancelled signal sample.

Thus, an improved noise-cancelled signal sample is provided with low latency and low complexity. According to a second aspect, the present disclosure relates to a noise cancellation method for processing a plurality of input samples being affected by noise, wherein the plurality of input samples comprising at least a first input sample and a second input sample.

In a further embodiment, the plurality of input samples comprises more than two samples such as <NUM> input samples.

The noise cancellation method comprises method steps that correspond to the function performed by the structural elements of the noise canceller.

Thus, an improved method is provided, which allows cancelling noise for input samples and delivering a noise-cancelled sample more efficiently and effectively.

According to a third aspect the present disclosure relates to a computer program product which comprises a computer program for performing the steps of the noise cancellation method according to the second aspect when performed by a processing device.

Further embodiments of the present disclosure will be described with respect to the following figures, wherein:.

In the various figures, identical reference signs will be used for identical or at least functionally equivalent features.

In the following description, reference is made to the accompanying drawings, which form part of the disclosure, and in which are shown, by way of examples, specific aspects in which the present disclosure may be placed. It is understood that other aspects may be utilized and structural or logical changes may be made without departing from the scope of the present disclosure. The following detailed description, therefore, is not to be taken in a limiting sense, as the scope of the present disclosure is defined be the appended claims.

Any transmission systems can be modelled as presented in <FIG>, wherein an exemplary transmission system <NUM> is shown and the transmitted signal x is modified by transfer functions of devices 101a-c. These devices 101a-c can be a digital-to-analogue converters modulator drivers, photo detectors etc. Each device can be represented by a transfer function that can be linear or nonlinear or a combination thereof. Active devices normally add a noise n that can follow, for instance, a Gaussian function. In general, noise is not a Gaussian noise and the system is nonlinear. Often, the total transfer function is approximated by a linear system with an additive Gaussian noise. The output signal y is convolution of the signal x and the system pulse response h: <MAT> <MAT> wherein z represents the signal y with noise n. In this case, linear and nonlinear equalizers can be used. The feedforward equalizer (FFE) <NUM> introduces noise amplification at frequencies that have low signal-to-noise ratio (SNR). In order to decrease this effect, techniques of noise compression or noise cancellation can be used. After the feedforward equalizer (FFE) <NUM>, the output signal can then be processed by using forward error correction (FEC) <NUM>.

The receiver uses an equalizer to repair the signal x effected by the transmission system. A preferred equalizer is the feedforward equalizer (FFE) due to its low latency and complexity. The full-response FFE noise amplification effect is presented in <FIG> for low-pass systems. As can be seen from <FIG>, the signal after the transmission system and before the feedforward equalizer (FFE) has a low-pass effect, i.e. the high frequency components are more attenuated than the low frequency components. The feedforward equalizer (FFE) flattens the spectrum of the FFE decisions and busts the high-frequency noise after the FFE that makes performance worse.

In general, the feedforward equalizer can be nonlinear, e.g. it can be the Volterra filter. To suppress the equalizer noise, a feedback noise canceler can be used. It uses the previous K decisions, calculates noise samples w that are filtered by a noise filter with coefficients ai, j=<NUM>,<NUM>,. Predicted noise sample np is added to the FFE output signal y and the decision is made by a quantizer. The coefficients a are calculated by the algorithm that uses autocorrelation functions and solves a set of Yule-Walker equations. The FFE noise amplification can be handled by using the following methods:
A first method comprises the feedforward equalizer (FFE) and the decision feedback equalizer (DFE), wherein the decision feedback equalizer (DFE) does noise whitening after the feedforward equalizer (FFE) but suffers from error propagation. When system bandwidth is very narrow, some precoding techniques are necessary, e.g. differential precoding. The main obstacle is complexity and latency.

A second method comprises the feedforward equalizer (FFE), a post-filter (PF) and the maximum likelihood sequence estimator (MLSE), wherein the feedforward equalizer (FFE) does full response equalization, a post-filter (PF) is a matched filter before and the maximum likelihood sequence estimator (MLSE). This method provides the best performance but includes the highest complexity and latency.

A third method comprises feedforward noise cancellation (NC), wherein this method has similar performance as the decision feedback equalizer (DFE). This low-latency and low-complexity algorithm seems to be the best choice for medium system bandwidth limitations. In strong bandwidth limitation scenarios, this method shows bad performance at high signal-to-noise ratio values that can be worse than the performance of the decision feedback equalizer (DFE).

The noise cancellation consists of several stages. An exemplary noise cancellation <NUM> is shown in <FIG>, wherein the noise cancellation has k stages 301a-c. The first stage gets the signal y i.e. an output of the feedforward equalizer (FFE). Each stage tries to improve decisions yd and N decisions are derived in one block processing module. Each block uses <NUM>M overlapping symbols.

As an example, the first stage <NUM> of a noise cancellation for a single symbol is shown in <FIG>. The Q block 403a-d is a slicer which makes decisions. The noise samples w<NUM> are multiplied by coefficients a 411a-d and summed up with the input sample <NUM> y. The noise-cancellation output signal z is quantized in the next stage to get noise samples w<NUM>. The coefficients a are calculated by the algorithm that uses autocorrelation functions and solves a set of Yule-Walker equations to design a noise predictor. The stages that do not improve decisions can be bypassed.

<FIG> shows further a processing device that serves as a "noise canceller" <NUM> according to an embodiment in a communication system for processing a plurality of input samples being affected by noise.

As can be taken from the detailed view shown in <FIG>, the processing device (i.e. the "noise canceller" <NUM>) further comprises an input buffer <NUM>, one or more signal slicers <NUM>, one or more subtractors <NUM>, a superimpose <NUM>, a noise cancellation stage <NUM>, a subsequent noise cancellation stage <NUM>, an error analyzer <NUM> and one or more multipliers <NUM>. The functions and advantages of these entities in the noise canceller <NUM> will be further discussed in detail in the following.

In an embodiment, the plurality of input samples comprises at least a first input sample and a second input sample and the noise cancellation stage <NUM> is configured to buffer the first input sample and the second input sample, and to determine a first decision sample representing the first input sample and a second decision sample representing the second input sample.

After obtaining the first and second decision samples, the noise cancellation stage <NUM> is configured to determine a first noise sample from a difference between the first input sample and the first decision sample and to determine a second noise sample from a difference between the second input sample and the second decision sample. Also, the noise cancellation stage <NUM> determines a sign pattern comprising a sign of the first decision sample and a sign of the second decision sample.

Once a sign pattern is determined, the noise cancellation stage <NUM> processes the first noise sample and the second noise sample in dependence on the sign pattern according to an embodiment, to obtain a first processed noise sample and a second processed noise sample.

Finally, in an embodiment, the noise cancellation stage <NUM> superimposes the first processed noise sample and the second processed noise sample to obtain a noise-cancelled sample.

In a further embodiment, the input buffer <NUM> of the noise canceller <NUM> is configured to buffer the input signal samples.

In a further embodiment, the one or more signal slicers <NUM> of the noise canceller <NUM> are configured to provide the decision samples, in particular hard-decided decision samples.

In a further embodiment, the one or more subtractors <NUM> of the noise canceller <NUM> are configured to determine the first noise sample and the second noise sample.

In a further embodiment, the superimposer <NUM> of the noise canceller <NUM>, in particular an adder, is configured for superimposing the first processed noise sample and the second processed noise sample.

If the plurality of samples further comprises a third input sample, according to a further embodiment, the noise cancellation stage <NUM> is further configured to determine the sign pattern comprising the sign of the first decision sample, the sign of the second decision sample and the sign of the third input sample. Moreover, the noise canceller <NUM> or the superimposer <NUM> of the noise canceller <NUM> is configured to superimpose the first processed noise sample, the second processed noise sample and the third input sample or a third decision sample representing the third input sample to obtain a noise-cancelled sample.

In a further embodiment, the subsequent noise cancellation stage <NUM> of the noise canceller <NUM> is configured to receive noise-cancelled signal samples from the noise cancellation stage <NUM>, the received noise-cancelled signal samples forming a first input sample and a second input sample for sample processing in the subsequent noise cancellation stage <NUM>.

In a further embodiment, the subsequent noise cancellation stage <NUM> is further configured to determine a first decision sample representing the first input sample and a second decision sample representing the second input sample; determine a first noise sample from a difference between the first input sample and the first decision sample, determine a second noise sample from a difference between the second input sample and the second decision sample; determine a sign pattern comprising signs of the first decision sample and the third decision sample; process the first noise sample and the second noise sample in dependence on the sign pattern to obtain a first processed noise sample and a second processed noise sample; and superimpose the first processed noise sample and the second processed noise sample to obtain a noise-cancelled signal sample.

In a further embodiment, the noise canceller <NUM> is configured to select a predetermined processing scheme from a plurality of processing schemes upon the basis of the sign pattern in order to process the first noise sample and the second noise sample. Alternatively, the error analyzer <NUM> is configured to determine a processing scheme for processing the noise samples in dependence on the sign pattern, and the error analyzer <NUM> is further configured to output weighting parameters according to the determined processing scheme for weighting the noise samples.

In a further embodiment, the noise canceller <NUM> is configured to determine weighting parameters in dependence on the sign pattern and the one or more multipliers <NUM> are configured to weight the first noise sample and the second noise sample with the respectively determined weighting parameter. The noise canceller <NUM> is configured to multiply the first noise sample by a first weighting parameter and to multiply the second noise sample by a second weighting parameter or to shift binary values of the first noise sample and binary values of the second noise sample by a number of binary positions in order to weight the first noise sample and the second noise sample.

Alternatively, the noise canceller <NUM> is configured to select predetermined weighting parameters from a predefined parameter set in dependence on the sign pattern, and to weight the first noise sample and the second noise sample with the selected predetermined noise parameters. In an embodiment, the one or more multipliers <NUM> of the noise canceller <NUM> are configured to weight the first noise sample and the second noise sample by the same predetermined weighting parameter or by different weighting parameters if the sign pattern indicates that the signs of the first noise sample and the second noise sample are equal.

In a further embodiment, the noise canceller <NUM> is configured to compare an absolute value of the first noise sample to an absolute value of the second noise sample, to determine a first weighting parameter and a second weighting parameter such that the first weighting parameter is equal to or greater than the first weighting parameter if the absolute value of the first noise sample is smaller than the absolute value of the second noise sample, to weight the first noise sample with the first weighting parameter, and to weight the second noise sample with the second weighting parameter.

In a further embodiment, the noise canceller <NUM> is configured to zero or to discard the first noise sample and/or the second noise sample in order to output the third input signal sample as the noise-cancelled signal sample if the sign pattern indicates different signs of the first noise sample and the second noise sample.

Embodiments of the disclosure provide a novel probabilistic noise cancellation (PNC) which is based on a multi-stage feedforward noise cancellation. In particular, embodiments of the disclosure use an error analyzer 603a-c to set the coefficients of the noise predictor as shown in <FIG>.

According to an embodiment shown in <FIG>, the probabilistic noise cancellation consists of k stages 601a-c, and the coefficients of the noise predictor are not a and are not fixed anymore as in the prior art noise cancellation as already mentioned in the above <FIG>. They are calculated based on error observations at the considered symbol and a few neighboring symbols. The coefficients a are weighted based on the error observations snd (signs of errors after the hard slicer) and the stage number i , that is, b is a function of a, snd, and i : b=a·c(sndi,i). The error analyzer 603a-c considers error observations sndi, i=<NUM>,. ,<NUM>U-<NUM>, takes into account the stage number and generates the weighting factor c. The parameter U defines the number of considered noise samples. The output can be generated: <MAT> where cL denotes a weighting factor for the future noise samples and cR is a factor related to the past noise samples (see <FIG> in the following). It holds a(-j) = a(+j) but b(-j) and b(+j) can be different. It is to be noted that the factors cL and cR depend on j (error position).

In an embodiment, a first stage <NUM> for single symbol in probabilistic noise cancellation (PNC) is shown in <FIG>, wherein the Q block 703a-d is signal slicers that make decisions The noise samples w<NUM> are multiplied by coefficients b 711a-d and summed up with the input sample <NUM> y.

Most communication systems has a low-pass characteristics that can be approximated by <NUM>+αD function, where D is a delay of one symbol interval and α is a value between <NUM> and <NUM>. In transmission systems using appropriate components, the value of α can be in a range from <NUM> to <NUM> (see <FIG>). Therefore, the noise after the feedforward equalizer (FFE) has a shape described by <NUM>-αD (see <FIG>).

In an embodiment, let us denote noise samples derived by using the transmitted symbols s by ns=y-s where y is output of the feedforward equalizer (FFE). By nd=y-yd we denote noise samples obtained by using the decisions (w in <FIG>). We deal with signs of these errors sns=sign(ns) and snd=sign(nd) that are binary encoded (-<NUM>=><NUM> and +<NUM>=><NUM>). Let us consider only three consecutive samples for the case of α=<NUM> exactly at decision error positions for the full response equalizer. The numbers of these events (<NUM> different sign patterns) are presented in <FIG>.

Because of errors, snd has very high value at error positions for low probably error patterns. For example sns(-+-) and sns(+-+) have very high probability. Errors often happens at these positions and these error patterns are then converted after a slicer into snd(---) and snd(+++).

For the full response equalizer, the most errors happen for snd(<NUM>) and snd(<NUM>) and in the first stage of the probabilistic noise cancellation (PNC) only these events should be corrected. It means only if the error patterns ---and +++ are detected the noise predictor will be used. In this case, we can use cl (- - ,<NUM>) = cl(+ + +,<NUM>) = <NUM> (cl(<NUM>,<NUM>) = cl(<NUM>,<NUM>) = <NUM>) and eliminate most of errors. Of course, some errors will be generated, however, some of them will be eliminated in next stages. The number of snd events at positions <NUM> and <NUM> after the first iteration shown in <FIG> indicates that many of these events are corrected and they disappeared. The ratio of good and bad corrections depends on the parameter α and SNR. In our simulated case, this number was close to <NUM>. It indicates that the efficiency of the first iteration is very high.

Embodiments of the disclosure comprises several steps:.

Finally, the coefficients cL(i,m,v) and cR (i,m,v) are optimized in order to get a minimum bit error rate.

According to an embodiment, the probabilistic noise cancellation (PNC) can be significantly simplified using only <NUM>-noise pattens and the probabilistic noise cancellation without multiplications in stages above the stage <NUM>. The error patterns -+- (error pattern <NUM>) and +-+ (error pattern <NUM>) rarely happen when the full response equalizer is used and the noise predictor is not so efficient for these noise patterns. However, when a partial response equalizer is used, these noise patterns often happen and the probabilistic noise cancellation is very useful in this case.

Therefore, only error patterns -x- and +x+ will be processed in the first stage, where x represents signs - and +. Using the noise patterns from <NUM> to <NUM>, the noise predictor coefficients in stage i are calculated based on the following rules:.

Noise patterns <NUM>, <NUM>, <NUM>, and <NUM> => cL and cR are optimized. At least one of them is not equal to <NUM>.

Noise patterns <NUM>,<NUM>, <NUM>, and <NUM>:.

The values of cR and cL can be limited to the set (<NUM>, ¼, ½, <NUM>) without a noticeable loss of performance. This limitation decreases the complexity of the probabilistic noise cancellation (PNC) as the multiplication by this value is done just by the decimal point shifting.

According to a further embodiment, <NUM>-tap <NUM>-stage PNC provides satisfactory performance. More stages and taps bring negligible gain.

According to a further embodiment, multipliers will be used only in the first stage and multipliers output values will be buffered. Moreover, noise weighted values are calculated in higher stages only by adders.

According to a further embodiment, only decimal point shift, sign changing, and addition are required in the stages above the first stage.

<FIG> shows a schematic diagram illustrating a noise cancellation method <NUM> for processing a plurality of input samples being affected by noise, wherein the plurality of input samples comprising at least a first input sample and a second input sample.

The noise cancellation method <NUM> comprises the following steps:.

While a particular feature or aspect of the disclosure may have been disclosed with respect to only one of several implementations or embodiments, such feature or aspect may be combined with one or more other features or aspects of the other implementations or embodiments as may be desired and advantageous for any given or particular application. Furthermore, to the extent that the terms "include", "have", "with", or other variants thereof are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprise". Also, the terms "exemplary", "for example" and "e.g." are merely meant as an example, rather than the best or optimal. The terms "coupled" and "connected", along with derivatives may have been used. It should be understood that these terms may have been used to indicate that two elements cooperate or interact with each other regardless whether they are in direct physical or electrical contact, or they are not in direct contact with each other.

Claim 1:
Noise canceller (<NUM>) for processing a plurality of input samples being affected by noise, the plurality of samples comprising at least a first input sample and a second input sample, the noise canceller (<NUM>) comprising a noise cancellation stage (<NUM>), the noise cancellation stage (<NUM>) being configured to:
buffer the first input sample and the second input sample;
determine a first decision sample representing the first input sample and a second decision sample representing the second input sample;
determine a first noise sample from a difference between the first input sample and the first decision sample;
determine a second noise sample from a difference between the second input sample and the second decision sample;
characterized in that the noise cancellation stage (<NUM>) is configured to:
determine a sign pattern comprising the sign of the first decision sample, the sign of the second decision sample, and the sign of a third input sample for which noise is predicted
determine weighting parameters for predicting noise in dependence on the sign pattern;
process the first noise sample and the second noise sample in dependence on the sign pattern, to obtain a first processed noise sample and a second processed noise sample, wherein the first noise sample and the second noise sample are weighted with the respectively determined weighting parameter; and
superimpose the first processed noise sample, the second processed noise sample and the third input sample or a third decision sample representing the third input sample to obtain a noise-cancelled sample.