Method and apparatus for hybrid decision feedback equalization

A method and apparatus for a decision feedback equalizer wherein a correction term is used to compensate for slicer errors, thus avoiding error propagation. Filter coefficients for the equalizer are selected so as to minimize a cost function for the equalizer. The cost function calculation includes a correction term. The correction term is a function of the energy of the filter coefficients. In one embodiment, the cost function includes a Mean Squared Error (MSE) calculation. The equalizer includes a coefficient generator responsive to the correction term. The correction term may depend on the Signal-to-Interference-and-Noise Ratio (SINR) at the output of the equalizer.

REFERENCE TO CO-PENDING APPLICATION FOR PATENT

The present Application for Patent is related to “Soft Slicer in a Hybrid Decision Feedback Equalization” by Srikant Jayaraman et al., having application Ser. No. 10/199,158, filed concurrently herewith, and assigned to the assignee hereof.

BACKGROUND

The present invention relates generally to equalization of a received signal, and more specifically to hybrid decision feedback equalization.

The transmission of digital information typically employs a modulator that maps digital information into analog waveforms. The mapping is generally performed on blocks of bits contained in the information sequence to be transmitted. The waveforms may differ in amplitude, phase, frequency or a combination thereof. The information is then transmitted as the corresponding waveform. The process of mapping from the digital domain to the analog domain is referred to as modulation.

In a wireless communication system, the modulated signal is transmitted over a radio channel. A receiver then demodulates the received signal to extract the original digital information sequence. At the receiver, the transmitted signal is subject to linear distortions introduced by the channel, as well as external additive noise and interference. The characteristics of the channel are generally time varying and are therefore not known a priori to the receiver. Receivers compensate for the distortion and interference introduced by the channel in a variety of ways. One method of compensating for distortion and reducing interference in the received signal employs an equalizer. Equalization generally encompasses methods used to reduce distortion effects in a communication channel. From the received signal, an equalizer generates estimates of the original digital information.

Current equalization methods are based on assumptions regarding the received signal. Such assumptions are generally not correct over a variety of coding, modulation and transmission scenarios; and, therefore, these equalizers do not perform well under many conditions. Additionally, current equalizers employing decision feedback often suffer from error propagation effects that amplify the effect of isolated decision errors. Additionally, the decision feedback process involves hard decisions regarding each symbol and does not consider the likelihood that a symbol decision is correct.

There is, therefore, a need in the art for an equalization method that reduces linear distortion in a received signal over a variety of operating conditions. Still further, there is a need to reduce error propagation in a decision feedback equalizer. Additionally, there is a need to provide a likelihood measure to the decision feedback process.

DETAILED DESCRIPTION

FIG. 1Aillustrates a portion of the components of a communication system100. Other blocks and modules may be incorporated into a communication system in addition to those blocks illustrated. Bits produced by a source (not shown) are framed, encoded, and then mapped to symbols in a signaling constellation. The sequence of binary digits provided by the source is referred to as the information sequence. The information sequence is encoded by encoder102which outputs a bit sequence. The output of encoder102is provided to mapping unit104, which serves as the interface to the communication channel. The mapping unit104maps the encoder output sequence into symbols y(n) in a complex valued signaling constellation. Further transmit processing, including modulation blocks, as well as the communication channel and analog receiver processing, are modeled by section120.

FIG. 1Billustrates some of the details included within section120ofFIG. 1A. As illustrated inFIG. 1B, the complex symbols y(n) are modulated onto an analog signal pulse, and the resulting complex baseband waveform is sinuosoidally modulated onto the in-phase and quadrature-phase branches of a carrier signal. The resulting analog signal is transmitted by an RF antenna (not shown) over a communication channel. A variety of modulation schemes may be implemented in this manner, such as M-ary Phase Shift Keying (M-PSK), 2M-ary Quadrature Amplitude Modulation (2MQAM), etc.

Each modulation scheme has an associated “signaling constellation” that maps one or more bits to a unique complex symbol. For example, in 4-PSK modulation, two encoded bits are mapped into one of four possible complex values {1,i,−1,−i}. Hence each complex symbol y(n) can take on four possible values. In general for M-PSK, log2M encoded bits are mapped to one of M possible complex values lying on the complex unit circle.

Continuing withFIG. 1A, at the receiver, the analog waveform is down-converted, filtered and sampled, such as at a suitable multiple of the Nyquist rate. The resulting samples x(n) are processed by the equalizer110which corrects for signal distortions and other noise and interference introduced by the channel, as modeled by section120. The equalizer110outputs estimates of the transmitted symbols ŷ(n). The symbol estimates are then processed by a decoder to determine the original information bits, i.e., the source bits that are the input to encoder102.

The combination of a pulse-filter, an I-Q modulator, the channel, and an analog processor in the receiver's front-end, illustrated inFIG. 1AandFIG. 1B, is modeled by a linear filter106having an impulse response {hk} and a z-transform H(z), wherein the interference and noise introduced by the channel are modeled as Additive White Gaussian Noise (AWGN).

FIG. 1Bdetails processing section120as including a front end processing unit122coupled to baseband filters126and128for processing the In-phase (I) and Quadrature (Q) components, respectively. Each baseband filter126,128is then coupled to a multiplier130and132, respectively, for multiplication with a respective carrier. The resultant waveforms are then summed at summing node134and transmitted over the communication channel to the receiver. At the receiver, an analog pre-processing unit142receives the transmitted signal, which is processed and passed to a matched filter144. The output of the matched filter144is then provided to an Analog/Digital (A/D) converter146. Note that other modules may be implemented according to design and operational criteria. The components and elements ofFIG. 1A and 1Bare provided for an understanding of the following discussion and are not intended to be a complete description of a communication system.

As discussed hereinabove, the sequence of symbols transmitted are identified as {y(n)}. For the present discussion, assume the symbols {y(n)} are normalized to have mean unit energy, i.e., E|yn|2=1. If the channel output were filtered and sampled at the symbol rate (which may or may not be the Nyquist rate), the channel output is given as:

xn=∑k⁢hk⁢yn-k+ηn(0)
where ηnis white Gaussian noise with variance (Es/N0)−1. The equalizer is usually implemented as a linear filter with coefficients {fk} and defined by a z-transform F(z). Let ŷndenote the equalizer's output, wherein ŷnis given as:

ηn′=∑k⁢fk⁢ηn-k⁢.(2⁢a)
Note that the second term within square brackets, [. . . ], of Equ. (2) represents the Inter-Symbol Interference (ISI) and noise. The first term of Equ. (2) corresponds to the interference associated with past symbols, while the second term corresponds to the interference associated with future symbols. The first term is often referred to as “causal” ISI, whereas the second term is often referred to as “anti-causal” ISI. If the designer assumes the past symbols are detected correctly, the causal ISI term may be removed. In an ideal case, if the equalizer has knowledge of the constellation symbols yn−1, yn−2, . . . , i.e., constellation symbols transmitted prior to time n, when determining the estimate ŷn, the equalizer can remove part of the inter-symbol interference by subtracting the first term of [. . . ] of Equ. (2). In practical systems, however, the equalizer only has knowledge of the symbol estimates previously generated, such as ŷn−1, ŷn−2, . . . . If the interference and noise are small enough, it is reasonable to expect that symbol decisions on the estimate ŷnwill yield the original transmitted constellation symbol yn. A device making such symbol decisions is referred to as a “slicer” and its operation is denoted by σ(.). The receiver could then form an estimate of the causal ISI using the sequence of symbol decisions from the slicer, and subtract this estimate from the equalizer's output to yield:

FIG. 3illustrates a communication system350employing a Decision Feedback Equalizer (DFE)340. The communication system350is modeled as having an equivalent linear channel352, which filters the sequence of symbols yn. Noise and interference, ηn, is added at summing node354, and the output, xn, denotes the signal samples as received after front-end processing and sampling at the receiver. The DFE340processes xn, and filters xnto generate the estimate ŷn. The DFE340is modeled as having a linear feedforward filter356and a linear feedback filter358. The feedforward filter356has tap coefficients designated as {fk} and implements the z-transform F(z). The DFE340also includes a purely causal feedback filter358coupled to a slicer360forming a feedback loop generating an estimate of causal ISI. In other words, the feedback filter358removes that part of the ISI from the present symbol estimate caused by previously detected symbols. The causal ISI estimate from the feedback filter358is provided to a summing node308which subtracts the causal ISI estimate from the output of the feedforward filter356. The resultant output of the summing node308is the equalizer output ŷn. The equalizer output ŷnis also an estimate of the transmitted symbol ynand is provided to decoder364for determining the original information sequence.

The slicer360processes the equalizer output from the summing node308and in response makes a decision as to the original symbol yn. The output of the slicer360is then provided to the purely causal feedback filter358. The feedforward filter356is also referred to herein as a Feed Forward Filter (FFF). The feedback filter358is also referred to herein as a Feed Back Filter (FBF). In a DFE, optimization of the filter coefficients, both feedforward filter356and feedback filter358, directly affects performance of the equalizer. The device which performs this optimization is designed as Coefficient Optimizer362inFIG. 3. There are a variety of methods available for optimizing the filter coefficients. Traditionally, the FFF and FBF coefficients are optimized under the implicit assumption that the slicer's symbol decisions are perfectly reliable and that causal ISI, i.e., the interference from past symbols, is removed perfectly by the FBF. Under this assumption, the FFF coefficients are optimized such that the residual interference and noise term in Equ. (3) is small. More precisely, the z-transform of the FFF, F(z), is optimized so that ŷnin Equ. (3) is close to ynin a mean-square sense.

In practice, the FFF and FBF are often implemented by Finite Impulse Response (FIR) filters and during an initial training/preamble/adaptation period, the FFF and FBF are “trained” on pilot symbols by assuming perfect slicer performance, i.e., σ(ŷn)=yn. This is accomplished by by-passing the slicer and feeding back locally generated (and hence correct) pilot symbols, rather than sliced (hence possibly erroneous) pilot symbol decisions, into the FBF. A variety of algorithms may be implemented for filter coefficient optimization during the training period, including adaptive algorithms such as Least Mean Square (LMS), Recursive Least Squares (RLS), direct matrix inversion, as well as others. Once the training period is completed, the slicer360is engaged and the sliced data symbols are fed back through the FBF.

Conventional DFE optimization algorithms introduce a variety of potential problems. For systems employing strong coding, the slicer decisions often have a large Symbol Error Rate (SER). For example, an SER of 25% or more is not uncommon for a system employing a medium size constellation, such as 16-QAM, and a low rate turbo code, such as rate of ⅓, when operating at the 1% packet error rate point. On the other hand, the DFE's FFF and FBF coefficients are conventionally optimized under the incorrect assumption that the slicer's decisions are perfectly reliable.

Additionally, the FFF and FBF coefficients are optimized assuming the causal ISI is perfectly removed. As a result, the anti-causal ISI is reduced at the expense of greater causal ISI. Conventional DFE optimization algorithms, in terms of the equations provided herein (specifically, Equs. (1)–(3)), lead to gkvalues which tend to be large for k>0, but small for k<0. When the slicer SER is not negligible, however, erroneous symbol decisions infect the FBF and are thereafter subtracted incorrectly. When the gkvalues for k>0 are large, the residual interference is thus amplified, possibly resulting in further slicer errors on subsequent symbols. This phenomenon is called error propagation.

Attempts to mitigate error propagation include feeding back sliced pilot symbols during training, as opposed to training the FFF and FBF by feeding back locally generated (hence correct) pilot samples. The sliced pilot symbols are occasionally in error, forcing the FFF and FBF to adjust accordingly. This method is not without problems. The sliced pilot symbols and sliced data symbols may incur very different error rates as the pilot symbols are typically transmitted via BPSK, i.e., 2-PSK, (or another smaller constellation) but the data symbols are typically transmitted via a larger constellation. As a result, the SER of the pilot symbols and data symbols might be quite different. In this case, as the FFF and FBF coefficients are optimized based on the sliced pilot symbols, the effect of those coefficients in processing the data symbols results in suboptimal performance.

These problems are resolved by optimizing the FFF and FBF coefficients to account for errors caused by the slicer360ofFIG. 3. In other words, the Coefficient Optimizer362is modified to recognize that the causal ISI may not be removed perfectly due to slicer errors. This approach differs from prior methods which implicitly assume the slicer is error-free and, therefore, that causal ISI is perfectly removed.

The theory behind one embodiment is to model the slicer operation by an independent, identically distributed (i.i.d.) “channel”, labeled Q({tilde over (y)}|y). The “channel” is assumed independent of the noise process designated as {ηn} in Equ. (0), and the transmitted symbol sequence designated as {yn}. This “channel” is completely characterized by its conditional density Q({tilde over (y)}|y) where {tilde over (y)} and y denote the slicer's output and the actual transmitted symbol, respectively. Assume such a channel is the cause of symbol errors in the FBF. In practice, symbol errors occur in bursts, because a slicer error on the current symbol implies following symbols may have an increased probability of being sliced incorrectly. In the simplified slicer model considered herein, the slicer errors are assumed i.i.d.

FIG. 2illustrates a conceptual model300of a communication system with a decision feedback equalizer. Symbols transmitted via the communication channel302modeled by transfer function H(z) are corrupted by additive noise at summing node304. The resulting signal is filtered by FFF306. An estimate of the original transmitted symbol is generated by subtracting an error term at summing node308. The estimate of the original transmitted symbol is available for decoder316. The error term is generated by a causal Feedback Filter310, with transfer function B(z), which filters the outputs of “channel” Q({tilde over (y)}|y)314. The error term generated by Feedback Filter310represents an estimate of the causal ISI present in the output of FFF306. The “channel” Q({tilde over (y)}|y) mimics the statistical behavior of slicer360inFIG. 3, i.e., the statistical relationship between the input and output of channel314is identical to the statistical relationship between the transmitted symbol ynand the corresponding output {tilde over (y)}n=σ(ŷn) of slicer360. The coefficient optimizer320is responsible for optimizing the filter coefficients for the FFF306and the FBF310. Note that the main difference betweenFIG. 3andFIG. 2is the replacement of the slicer360with the conceptual model of “channel” Q({tilde over (y)}|y)314.

As mentioned hereinabove, the slicer is modeled inFIG. 2by selecting “channel” Q({tilde over (y)}|y) so as to model the statistical behavior of an actual slicer while ignoring the statistical dependence in time of slicer errors. As the actual slicer operates on the output of the equalizer, it follows that the relevant marginal statistics involve residual interference. Let SINR represent the Signal-to-Interference-and-Noise ratio at the output of the equalizer, i.e., at the output of summing node308inFIG. 2. Assume the residual interference and noise at the equalizer output may be modeled as a zero-mean complex Gaussian random variable Z with independent real and imaginary parts, each with variance σ2, wherein:

σ2=12⁢(SINR).(6)
The marginal statistics are given by the equivalent channel Q({tilde over (y)}|y), wherein:
Q({tilde over (y)}|y)=Pr{σ(y+Z)={tilde over (y)}},(7)
wherein σ( ) denotes a minimum distance slicing function given as:

σ⁡(y^)=arg⁢⁢miny∈Y⁢y^-y2(8)
and Z in Equ. (7) is the zero-mean complex Gaussian random variable, modeling residual interference with properties described hereinabove.FIG. 4illustrates the channel Q({tilde over (y)}|y) modeled according to the assumptions and Equations provided hereinabove. Specifically, the mathematical description of Q({tilde over (y)}|y)314inFIG. 2is illustrated as system380. The input to the slicer384is denoted by ŷ and is modeled as the transmitted symbol y, corrupted by additive noise and interference. The noise and interference is modeled by complex Gaussian random variable Z. The slicer384implements a minimum distance slicing function as described in Equ. (8), resulting in slicer output marked {tilde over (y)}. The joint statistics connecting y and {tilde over (y)} constitute the full mathematical description of the model for “channel” Q({tilde over (y)}|y). The construction of the channel Q({tilde over (y)}|y) illustrated inFIG. 4is novel and differs from prior methods in that the noise Z may have a non-zero variance. Prior methods implicitly assume Z is identically equal to zero. Thus, this model for the slicer is assumed to make decision errors, in contrast to prior methods that assume the slicer is error-free.

Returning toFIG. 2, let fQand bQdenote the FFF and FBF coefficients selected so as to minimize the mean square error between the transmitted symbol yn(the input of channel302) and the symbol estimate ŷn(the output of summing node308). In other words, the coefficients fQand bQare “Wiener MMSE optimal”. For reasons that will be made clear herein below, these coefficients are referred to as “Wiener Hybrid DFE” coefficients. The coefficients fQand bQmay be determined by a standard Wiener-Hopf optimization and are defined by the following equation:

[fQbQ]=[RFρQ⁢RF,BρQ*⁢RF,BHRB]-1⁡[pF0],(4)
wherein RFdenotes the covariance of the contents of the FFF, RBdenotes the covariance of the contents of the FBF, RF,Bdenotes the cross-covariance of the contents of the FFF and the FBF, and pFdenotes the cross-covariance between the contents of the FFF and the transmitted symbol. These covariances and cross-covariances depend on the linear channel302described by H(z). Assuming the symbols in Y, i.e., the transmit constellation, are used with equal probability, then ρQis defined as:

ρQ=1Y⁢∑y∈Y⁢∑y~∈Y⁢[y~*⁢y]⁢Q⁡(y~|y)(5)
wherein /Y/ denotes the cardinality of Y. i.e., the number of possible symbols in the transmit constellation. Thus, for a given Q({tilde over (y)}|y) and channel with z-transform H(z), the MMSE coefficients fQand bQare determined by application of Equ. (4) and Equ. (5).

Recall that Q({tilde over (y)}|y) was defined according to Equ. (6) and Equ. (7) by hypothesizing a value of SINR at the equalizer output. Application of Equ. (4) and Equ. (5) then lead to MMSE coefficients fQand bQ. When these values for the FFF and FBF coefficients are used in the FFF306and FBF310inFIG. 2, the resulting SINR at the equalizer output may be different from the SINR value originally hypothesized. So the hypothesized SINR value may or may not be consistent. However, a consistent SINR value, and hence a consistent set of MMSE coefficients fQand bQ, can be found by iterating, i.e., by using the newly found SINR value to define a new “channel” Q({tilde over (y)}|y), finding a new set of corresponding MMSE coefficients, etc. This iterative process may be represented schematically as follows:
(SINR)0→(f0,b0)→(SINR)1→(f1,b1)→(SINR)2
In particular an iterative algorithm may be used for computing the Weiner Hybrid DFE. The algorithm of the present embodiment is illustrated inFIG. 5. The process400begins by setting n=0 at step402and selecting SINR0arbitrarily. The process continues by determining SINRnand computing ρ(SINRn) by applying Equs. (5), (6), and (7) at step404. The filter coefficients fn,bnare computed at step406by using Equ. (4). According to the present embodiment, the process computes SINRn+1=SINR(fn,bn, SINRn) at step408. Note that SINR(f ,b ,x) denotes the SINR at the output of the equalizer with FFF coefficients f, and FBF coefficients b, and a slicer channel Q(.|.) with SINR x. The slicer channel is defined by Equ. (6) and Equ. (7). If the process converges at decision diamond410, processing continues to step412to set the filter coefficients. If the process has not converged, processing returns to step404.

Note that as described in the iterative algorithm ofFIG. 5, the value of SINR0may be chosen arbitrarily. The two extremes, SINR0=0, SINR0=∞, correspond to starting with a totally unreliable slicer or a perfect slicer, respectively.

Note that ρ represents the correlation between the slicer's output and the actual transmitted symbol, and as such, ρ is a function of the equalizer's output SINR. If the equalizer's output is very noisy, the correlation is small. In this case, the slicer's symbol decisions are largely unreliable and an accurate estimate of the causal ISI is not possible. As expected, in this case, the algorithm ofFIG. 5converges to FFF and FBF coefficients which closely resemble those of a Linear Equalizer, i.e., one where the FBF coefficients are constrained to be zero. On the other hand, when the equalizer's output is nearly noiseless, the slicer's correlation ρ tends to be close to one. In this case, the algorithm ofFIG. 5converges to FFF and FBF coefficients which closely resemble those of an “ideal” DFE, i.e., a DFE with a perfectly reliable slicer. In between these extremes, the algorithm ofFIG. 5converges to FFF and FBF coefficients which are a “hybrid” of these two limiting extremes. This “hybridization” is accomplished automatically by the iterative algorithm. For this reason, the FFF and FBF coefficients so obtained are referred to as “Hybrid DFE” coefficients.

The embodiment(s) described heretofore require explicit knowledge of the channel H(z) in order to construct the various covariances and cross-covariances of Equ. (4). The Wiener Hybrid FFF and FBF coefficients are then determined by solving Equ. (4) for fQ,bQ. In practice, however, H(z) is typically not known at the receiver, so an alternate method for determining the Wiener Hybrid DFE coefficients for the FFF and FBF is desirable. An alternate embodiment, referred to as the Adaptive Hybrid DFE, does not require explicit knowledge of the channel H(z). First, define the Mean Squared Error (MSE) as:

MSE=[1N⁢∑n=1N⁢yn-fH⁢Xn-bH⁢Zn2]+αQ⁢b2.(9⁢d)
wherein:
αQ=1+λQ2−2ρQ.  (9e)
Note that αQmay be referred to as a “modified measure of energy of the feedback filter coefficients” or an “error correction term.” The RLS optimization may be performed on the pilot symbols present in the transmission.
Least Mean Square Algorithm: Another embodiment which optimizes Equ. (9c) is based on the Least Mean Square (LMS) algorithm. The Least Mean Square (LMS) algorithm recursively adjusts the FFF and FBF coefficients of the Hybrid DFE so as to minimize the MSE defined in Equ. (9c). For a fixed channel Q({tilde over (y)}|y), a Least Mean Squares (LMS) algorithm updates are given as:

fn+1=fn-μ⁢⁢E-⁢{∂MSE∂fn}(10⁢a)bn+1=bn-μ⁢⁢E-⁢{∂MSE∂bn}(10⁢b)
wherein MSE is defined in Equ. (9c), μ is the LMS step-size and E−denotes dropping the statistical expectation in the definition of Equ. (9c). Calculating the partial derivatives results in:
fn+1=fn+μXnen*;  (11)

When the value of μ is chosen suitably small, the sequence of iterations defined by Equ. (11) through Equ. (13) is stable and converges to the set of coefficients which solve Equ. (4). Notice that this sequence of iterations does not require explicitly estimating the covariances and cross-covariances in Equ. (4).

FIG. 6illustrates an LMS algorithm according to one embodiment. The algorithm500starts with selection of an initial SINR0value at step502. Additionally, the index k is initialized as k=0. At step504, the value of SINRkis estimated and ρ(SINRk) is calculated or determined from a pre-calculated Look-Up Table (LUT). Equ. (11) through Equ. (13) given hereinabove are calculated iteratively, based on the pilot symbols in the transmission, until a convergence criteria is met at step510. The result of such iteration determines the values for (fk,bk). At step508the process estimates SINRk+1, which is the SINR at the equalizer output when the FFF and FBF coefficients are (fk,bk). The estimation may be done using the pilot symbols in the transmission. The process then increments the index k. On convergence of SINRkat decision diamond510, the process continues to step512to apply the filter coefficients. Else, processing returns to step504.

Algorithm with Periodic Pilot Bursts: According to another embodiment, a communication system incorporates periodically transmitted pilot bursts which are used by receivers to adjust the filter coefficients in the receivers' equalizer. Such adjustment is often referred to as “training the equalizer”. An example of such a system is a system supporting High Data Rate (HDR) as defined in “TIA/EIA-IS-856 CDMA2000 High Rate Packet Data Air Interface Specification” (the IS-856 standard). In an HDR system, 96 pilot symbols are transmitted every 0.833 ms. Each group of 96 pilot symbols is referred to as a “pilot burst”. In between pilot bursts, the HDR system transmits data symbols intended for receivers.FIG. 7illustrates an algorithm for applying an LMS-based hybrid DFE in such a system. The algorithm600initially sets up SINR0as equal to 0 or ∞ at step602. The initial choice of SINR is not designated and may not be critical, though for the fastest convergence, SINR0equal to ∞ may be preferred. The index k is also initialized and set equal to 0. At step604the algorithm determines SINRk, and computes ρ(SINRk) or determines the necessary value by consulting a pre-calculated Look-Up Table. The initial values for f and b are set as f0=0 and b0=0 at step606. During the (k+1)-th pilot burst, the process iterates Equations (11) through (13) for all chips of the pilot burst, step608. In the present HDR example, the algorithm600iterates for 96 chips of the pilot burst and the final values of f and b are saved. At step610the process estimates SINRk+1, using the 96 chips of the preceding pilot burst. During the data portion following the (k+1) pilot burst, the saved values of f and b are loaded into the FFF and the FBF and the data symbols are equalized in standard decision-feedback fashion (step.612). At step614the process computes the value of ρ(SINRk+1) and increments k. The process continues to implement the algorithm during demodulation operations.

The algorithm ofFIG. 7is adaptive for slowly time-varying channels, as quasi steady state SINRk, and therefore, ρ(SINRk), are not expected to vary much over the convergence time of the LMS algorithm.

Soft Slicer: As discussed hereinabove, error propagation significantly limits the use of DFEs in communication systems employing channel coding. Because the causal ISI is cancelled by feeding back decisions on individual symbols, a single isolated decision error may lead to a burst of subsequent decision errors, greatly enhancing the residual interference at the equalizer's output. If the channel code is strong, the probability of a symbol decision error is non-negligible (typically on the order of 25 percent) and error propagation may have serious effects on the performance of the DFE. One method of avoiding the effects incurred by such error propagation is to recognize that the usual “minimum distance” slicer attaches no confidence-level to symbol decisions. In other words, conventional slicer decisions provide no measure of the accuracy or correctness of symbol decisions. If a decision were known to be of questionable accuracy, it might be better to avoid canceling that symbol's contribution to the post-cursor tail, rather than risk compounding the residual interference by subtracting an incorrect decision. In other words, symbol decisions of low accuracy should not be included in the feedback loop canceling causal ISI.

One embodiment of a slicer that incorporates a confidence-level into the decision process will be referred to herein as a “soft slicer.” One soft slicer is described by a mathematical model as explained hereinbelow. First, assume the input symbol to the slicer is given as:
ŷ=y+n(14)
where y is the transmitted symbol belonging to the constellation Ψ, and n consists of residual noise and intersymbol interference. Assume that y is uniformly distributed over Ψ so that all constellation points are transmitted with equal probability. Let L(y,{tilde over (y)}) be a loss function measuring the loss incurred when a slicer decides {tilde over (y)} when the transmitted symbol is y. An optimum slicer σ:ŷ→{tilde over (y)}, wherein “optimum” refers to a slicer which minimizes Expected loss, is given by Bayes Rule:

σ⁡(Y^)=arg⁢⁢miny~∈Ψ⁢⁢E⁢{L⁡(Y,y~)|Y^}(15)
For the Minimum Error Probability (MEP) loss function given as:

L⁡(y,y~)=[0,y=y~1,y≠y~(16)
the expected loss results in:

σ⁡(Y^)=arg⁢⁢maxy~∈Ψ⁢⁢Pr⁢{Y=y~|Y^}(18)
Additionally, assuming the interference n is a Gaussian random variable with zero mean and variance σ2, then:

σ⁡(Y^)=arg⁢⁢miny~∈Ψ⁢⁢Y^-y~2(19)
independent of σ2. This is a traditional “minimum distance” slicer, and although it is “Bayes-optimum” for the loss function of Equ. (16), the slicer may lead to error propagation for the reasons discussed hereinabove. An alternate slicer design considers the quadratic loss function:
L(y,{tilde over (y)})=∥y−{tilde over (y)}∥2(20)
which, unlike the MEP loss function, penalizes larger errors more significantly than smaller errors. Following from Equ. (15):

σ⁡(Y^)=arg⁢⁢miny~∈Ψ⁢⁢E⁢{Y-y~2|Y^}=E⁢{Y|Y^}(21)
and the conditional mean equals:

σ⁡(Y^)=∑y∈Ψ⁢y⁡[ⅇY^-y22⁢σ2∑y∈Ψ⁢ⅇY^-y22⁢σ2](22)
An important observation is that unlike the slicer of Equ. (19), the slicer of Equ. (22) requires an estimate of the interference and noise variance

σ2⁡(e.g.,σ2=12⁢(SIN⁢⁢R)).
Note also that the slicer of Equ. (22) corresponds to the centroid of the a posteriori distribution on the constellation symbols, i.e., the centroid of the term in square brackets [. . . ] in Equ. (22). Thus if σ2is large, the assumption of a uniform prior distribution on a symmetric constellation implies a nearly uniform posterior distribution, and hence the centroid is near zero. On the other hand, when σ2 is small, the posterior distribution has its mass concentrated on the actual transmitted symbol and its neighboring constellation points; the centroid is therefore, close to the transmitted symbol. The slicer in Equ. (22) is thus referred to as a “soft slicer”.

The soft slicer can be used in the adaptive Hybrid DFE with minimum modification. The FFF and FBF coefficients are chosen to optimize the following definition of MSE:
MSE=E|yn−fHXn−bHZn|2+(1−2ρQ+λQ2)∥b∥2(23)
where
ρQ=EQ{{tilde over (Y)}*Y}  (24a)
similar to Equ. (5), and λQ2is defined as:
λQ2=EQ{|{tilde over (Y)}|2}  (24b)
The “channel” Q({tilde over (y)}|y) is defined as:
Q({tilde over (y)}|y)=Pr{σ(y+Z)={tilde over (y)}}(25)
wherein σ(.) represents the soft-slicer defined in Equ. (22) and Z is complex gaussian noise defined in exactly the same way as in Equ. (7). Following an analogous development of the optimization scheme based on the LMS algorithm, we find Equs. (11), (12) and (13) unchanged, except that for the fact that αQ=1+λQ2−2ρQis computed based on Equs. (24a), (24b) and the soft-slicer defined in Equ. (25). As before, the leakage factor (1−2ρQ+λQ2) is SINR dependent and may be determined by a table lookup.

The LMS-based algorithm as described hereinabove requires no additional changes. During the pilot training portion of the slot, the adaptation is performed as before; during the data portion of the slot, the conditional mean slicer is used in place of the “hard”, minimum-distance slicer.

The computations involved in the soft slicer, namely Equ. (22), may be too complicated for some practical implementations. One embodiment simplifies the slicer design so as to restrict the slicer output to take on at most N values. Equivalently, this amounts to restricting the slicer input to take on at most N values. In other words, the slicer input Ŷ is quantized to one of N points using a quantizer defined by: Q:Ŷ→{Ŷ1, . . . , ŶN}. Then for k=1, . . . , N, σ(Ŷ) is computed as:
σ(Ŷ)=σk, ifQ(Ŷ)=Ŷk(26)
wherein:
σk=E{Y|ŶεQ−1(Ŷk)}.  (27)

The quantized slicer's operation can be summarized as: 1) quantize Ŷ to one of N possible values; and 2) use this value and knowledge of the SINR as indices in a lookup table to determine {overscore (Y)}=σ(Ŷ). Since the complexity in this design lies in step 1), a further simplification would be to restrict Ŷ1, . . . , ŶNto lie on a uniform square grid and then quantize Ŷ by quantizing its Real and Imaginary parts separately, using a “nearest neighbor” criterion. Such a slicer function may be implemented with simple logic, i.e., by first computing the nearest set of neighbors based on Ŷ's Real-coordinate, then computing the nearest neighbor in this subset based on Ŷ's Imaginary-coordinate. Additionally, the lookup table may be fairly coarse in SINR, with 1 dB steps sufficient for most implementations. For example, given {σk} lookup tables for SINR=5 dB and SINR=6 dB, the appropriate σkvalues for an intermediate SINR value of say, 5.4 dB, may be determined by suitably interpolating between the two LUTs. In other words, the appropriate σkvalues at intermediate SINR values may be generated within the slicer device, thus reducing the necessary memory/storage requirements.

As an illustration of the application of a soft slicer to a Hybrid DFE (HDFE), considerFIGS. 8A and 8B.FIG. 8Aillustrates an 8-PSK constellation, wherein 8 complex symbols represent the 3 encoded bits mapped for modulation. As illustrated, the circles represent the constellation points used for modulation at the transmitter. The “x” marks indicate the samples as received at the receiver and include noise and interference introduced during transmission. Note that the received samples do not necessarily match the actual constellation symbols. In this case, the receiver decides which constellation symbol was actually sent. Typically, the received points are concentrated around the actual transmitted constellation symbols.

One method of determining the transmitted symbol from the received samples is to divide the constellation map into pie slices, as illustrated inFIG. 8B. Here the constellation map is divided into 8 slices,702,704,706,708,710,712,714, and716. The slices are determined, for example, in accordance with a minimum distance metric, which uses the Euclidean distance or separation between two constellation points to select a boundary. A problem exists when the received sample is approximately equidistant (i.e., approximately on the boundary) between two constellation points. In this case, if the decision process were to select the wrong constellation symbol, this error would be propagated in the feedback loop of a DFE. To avoid such errors and the associated amplification in a DFE, a soft slicer is applied that outputs a value not necessarily at a constellation symbol. The soft slicer implicitly determines a confidence level from the received samples. The confidence level provides the system with guidance in evaluating the sample. If the confidence level is low, i.e., an error is likely, the sample is not emphasized in the feedback portion of the equalizer. If the confidence level is high, the sample is considered reliable, and therefore, a suitable symbol estimate derived therefrom may be used in the feedback portion of the equalizer.

FIG. 9Aillustrates a 2-PSK constellation map. Note that decisions made based on the minimum distance from a constellation symbol may result in errors for received samples such as that marked by the “x.” Application of a soft slicer according to one embodiment, divides the constellation map into rectangles as illustrated inFIG. 9B. As plotted, the rectangles, such as rectangle720, are semi-infinite in the y-direction and not all rectangles encompass constellation symbols. When the slicer's input sample falls within one of the semi-infinite rectangles, a conditional mean value is assigned. Effectively all points within the rectangle are mapped to a common value. This value represents the conditional mean of the transmitted symbol, given the slicer's input sample falls within the rectangle of interest. The mapping of each rectangle to a corresponding conditional mean value is a function of the Signal to Interference and Noise Ratio (SINR). For example, a given rectangle may map to σ for SINR at a first level, e.g., SINR=4 dB. The same rectangle may map to σ′ for SINR at a second level, e.g., SINR=5 dB. The mapping and associated conditional mean values are stored in lookup tables for easy retrieval. An alternate embodiment calculates the conditional mean value according to a predetermined algorithm. Note that a square or rectangular grid is easily implemented and extensible to more complex constellations.

FIG. 10illustrates an Equalizer800using a soft slicer. The Equalizer800includes an FFF802coupled to a summing node804. The FFF802is controlled by an adaptive equalization algorithm808. The adaptive control unit808is responsive to an SINR estimation unit816. In an alternate embodiment, the SINR estimation unit816may be implemented as an MSE estimation unit. The SINR estimation unit816provides an SINR estimate to a lookup table (LUT)810. The SINR estimate is used in conjunction with the values stored in LUT810to determine αQ(SINR)=1+λQ2−2ρQdefined according to Equs. (24a), (24b) and (25). The adaptive equalization algorithm808uses the αQvalue produced from LUT810to update the coefficients of the FFF802and the FBF806, by iterating Equs. (11), (12) and (13). Recall that Equs. (11), (12) and (13) were based on the LMS algorithm and designed to optimize the MSE cost function defined in Equ. (23). In an alternate embodiment, the adaptive equalization algorithm808may implement another adaptive filtering algorithm, such as RLS, to optimize the MSE cost function defined in Equ. (23). The FBF806outputs an estimate of the causal ISI present in the output of the FFF802. The FBF806output is coupled to summing node804where it is subtracted from the output of FFF802. The output of summing node804, i.e., the estimate of transmitted symbol, is then provided to a decoder820, the SINR/MSE estimation unit816, and to the soft slicer812. The soft slicer812receives the SINR/MSE estimate from the SINR/MSE estimation unit816and generates a further estimate of the transmitted symbol, and outputs this further symbol estimate for filtering in FBF806.

FIG. 11is a flow chart of a soft slicer process incorporating a soft slicer according to one embodiment. The process first determines a region, such as a grid square or rectangle on the constellation map, corresponding to a quantization of the slicer input sample ŷ, at step902. A determination is made of the SINR value at step904. At step906, the process selects an appropriate mapping as a function of the SINR value. According to one embodiment, separate portions of a memory storage device store separate look up tables. The tables are accessed according to SINR value. At step908a conditional mean value is determined from the appropriate mapping and this is the slicer output.

Another soft slicer embodiment applies a square grid to the constellation map, and uses a Taylor expansion to generate a more accurate conditional mean value. In this embodiment, multiple smaller lookup tables store values corresponding to each SINR value. The process920is illustrated inFIG. 12. The region of the soft slicer input ŷ is determined at step921. At step922an SINR value is determined. The SINR value is used to determine appropriate mappings σ1(.) and σ2(.) at step924. The region of step920is mapped to a value σ1(ŷi), wherein i corresponds to the region. A second mapping is then performed at step926consistent with the SINR value and the region of step920to obtain σ2(ŷi). A conditional mean value is approximated at step928as σ1(ŷi)+(ŷ−ŷi)σ2(ŷi). The mappings σ1(.) and σ2(.) are closely related to the zero-th and first derivatives of σ(.) defined in Equ. (22).

FIG. 13illustrates a soft slicer954according to one embodiment. An SINR estimator952receives one or more symbol estimates and outputs an SINR estimate value SINR(n). The SINR(n) may be quantized in an optional quantizer956, and is provided to memory storage960, such as a LUT. A symbol estimate corresponding to the soft slicer input is also provided to a quantizer956, wherein the symbol estimate is quantized and the quantized value is used in conjunction with the SINR estimate to determine a corresponding value stored in the memory storage960. Note that in one embodiment, the information is stored in rows and columns, wherein the rows correspond to SINR values and the columns correspond to symbol values. Alternate embodiments, however, may store the information in any of a variety of ways, wherein the information is retrievable based on an SINR value and a symbol value. The values stored in the memory storage960may be the conditional mean of actual constellation symbol, given the soft slicer input estimate, such as defined in Equs. (22), (26) and. (27).FIG. 14illustrates a soft slicer980according to an alternate embodiment implementing a Taylor series computation. As illustrated, one or more received symbols are provided to an SINR estimator982and one symbol estimate, corresponding to the soft slicer input, is also provided directly to the soft slicer980. Note that the received symbols are corrupted by the transmission channel and therefore are herein also referred to as received “samples.” The SINR estimator982provides an SINR estimate SINR(n) to the soft slicer980. The SINR(n) may be provided to an optional quantizer986. The SINR(n), quantized or not, is provided to two memory storage units, A988and B990. The soft slicer input symbol estimate is provided to a quantizer984, the output of which is also provided to the memory storage units A988and B990. The memory storage units A988and B990store information used to compute the conditional mean values of the actual constellation symbol, given the soft slicer input symbol estimate. Such values may be the zero-th and first derivatives of the conditional mean of the actual constellation symbol, given the soft slicer input symbol estimate, such as given in Equs. (22), (26) and Equ. (27). The SINR(n) value and the quantized symbol value are used to identify the corresponding values in memory storage A988and B990. A summing unit992is used to implement the Taylor series computation. The soft slicer input symbol estimate, as well as the quantized value are provided to the summing unit992. In addition, the values stored in the memory storage units A988and B990are also provided to summing unit992. The summing unit992uses the inputs to compute an output that is a conditional mean estimate of the actual constellation symbol. While the present invention has been described with respect to a wireless communication system, such a system is provided merely as an example. The concepts described herein are applicable in a variety of communication systems, including, but not limited to wireline communication system, such as implementation in a wireline modem, etc. The present invention is applicable in a high data rate communication system, and allows optimization of resources and capacity in a data communication system by increasing receiver sensitivity and increasing the communication data rate. Those of skill in the art would understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.