Combined channel estimation and data detection using softstatistics for frequency-selective fast-fading digital links

A novel adaptive nonlinear equalizer for fast time-varying multipath channels that combines the channel estimation and data detection tasks is presented. The a posteriori probabilities (APPs) of the states of the intersymbol interference (ISI) channel are recursively computed from the received data by a symbol-by-symbol (SbS) detector and are then employed by a Kalman-type nonlinear channel estimator. Robust channel tracking and good data-detection performance are obtained, with a reasonable receiver complexity     

A novel adaptive receiver with enhanced channel tracking capabilityfor TDMA-based mobile radio communications

The paper presents a novel fast-adaptive nonlinear receiver which exploits soft statistics for tracking the random fluctuations experienced by time division multiple access (TDMA) mobile radio links impaired by frequency-selective time-variant multipath phenomena. The detection task is accomplished by an Abend-Fritchman-like symbol-by-symbol maximum likelihood (SbS-ML) detector which delivers both hard decisions and soft statistics in form of a posteriori probabilities (APPs) of the states of the intersymbol interference (ISI) channel. In the proposed adaptive receiver, these APPs are employed in place of the conventional hard-detected data to feed an ad hoc developed nonlinear recursive Kalman-type channel estimator. Extensive computer simulations show that the exploitation of soft statistics enhances the tracking capability of the channel estimator so that the proposed receiver generally outperforms the usual ones based on adaptive maximum likelihood sequence estimators (MLSEs) for signal-to-noise ratio (SNR) values over 12-13 dB. Furthermore, the experienced performance gap with respect to more complex per-survivor processing (PSP)-based multi-estimator detectors appears generally small on slowly and moderately fast time-varying channels characterized by values of the product Doppler bandwidth × signaling period B_DT_S below 5×10^-3     

A new family of soft-output adaptive receivers exploiting nonlinear MMSE estimates for TDMA-based wireless links

Decision-directed adaptive receivers suffer performance degradation on time varying and intersymbol interference-impaired links because of two major problems: the use of predicted channel estimates due to the unavoidable decision delay of any detector, and the unreliability of hard decisions used for channel estimation and tracking. It is shown here that combining a recursive nonlinear symbol estimator with a channel estimator with a low prediction order may alleviate this performance degradation. In particular, it is here proposed to employ the nonlinear minimum mean-square error (NL-MMSE) filtered and fixed-lag smoothed estimates of the transmitted symbols in place of the usual hard decisions for channel estimation and tracking. It is also shown that these NL-MMSE estimates can be recursively computed on the basis of a linear transformation of the vector of the a posteriori probabilities (APPs) of the states of the channel. This approach allows the prediction order of the channel estimates to be limited and, at the same time, limits the performance degradation due to erroneous hard decisions. Another result presented here is that the use of NL-MMSE estimates in place of hard decisions is not based on mere intuition only, but is a straightforward consequence of the statement of the problem of MMSE channel estimation when the overly optimistic assumption of correct decisions is dropped. On the basis of this novel approach, a new family of soft-output adaptive receivers is presented for time-division multiple-access-based radio communications. The proposed family of adaptive receivers is based on an APP-computer and exploits the APPs for both channel estimation and detection. The versatility of the APPs ensures that the architecture of the proposed receiver is flexible, so that several estimators and detectors can be embedded in it.     

An iterative beamforming and decoding procedure for wireless networks with uncoordinated channel access

We propose an iterative receiver algorithm to cope with nonstationary cochannel interference, as e.g. arising in wireless ad hoc networks with uncoordinated channel access. The receiver incorporates an adaptive beamforming by a sample matrix inversion (SMI) technique on the information-bearing signals from an array antenna, and an a posteriori probability (APP) computation. The APPs are utilized to successively reconstruct and eliminate the reliably decoded signal parts from the samples. This allows to perform the SMI on a small number of snapshots, for the sake of adaptability, while evading a dramatic performance degradation due to the presence of the signal of interest.     

Equalization of digital radio channels with large multipath delayfor cellular land mobile applications

A new maximum a posteriori (MAP) equalizer is proposed for digital radio links affected by large multipath delays. The “sparse” nature of the channel, where a few nonzero powerful taps are spaced by many negligible taps, is exploited to achieve a complexity proportional to the number of nonzero taps. When the channel is time-varying, an efficient nonlinear Kalman like channel estimator is employed to track only the nonzero taps     

Partial update and sparse adaptive filters

There has been increasing research interest in developing adaptive filters with partial update (PU) and adaptive filters for sparse impulse responses. On the basis of maximum a posteriori (MAP) estimation, new adaptive filters are developed by determining the update when a new set of training data is received. The MAP estimation formulation permits the study of a number of different prior distributions which naturally incorporate the sparse property of the filter coefficients. First, the Gaussian prior is studied, and a new adaptive filter with PU is proposed. A theoretical basis for an existing PU adaptive filter is also studied. Then new adaptive filters that directly exploit the sparsity of the filter are developed by using the scale mixture Gaussian distribution as the prior. Two new algorithms based on the Student's-t and power-exponential distributions are presented. The minorisation-maximisation algorithm is employed as an optimisation tool. Simulation results show that the learning performance of the proposed algorithms is better than or similar to that of some recently published algorithms     

一种用于无线信道的逐幸存处理均衡器

提出了一种适用于无线时变信道的逐幸存处理均衡器。通过训练序列得到信道参数的初始估计值,此后在Viterbi算法进行网格搜索的过程中,使得每一条幸存路径维持各自的信道参数,并在确定幸存分支后利用历史幸存序列对信道参数值进行更新,实现了信道参数的无时延估计。仿真结果表明,在无线时变信道环境下,逐幸存处理均衡器的性能明显优于其他传统均衡器。     

A new approach based on “soft statistics” to thenonlinear blind-deconvolution of unknown data channels

In this paper, we present a new nonlinear receiver for the blind deconvolution of intersymbol interference (ISI) impaired data. The proposed receiver achieves fast identification of an unknown transmission channel using only one channel estimator and requiring the computation of only the second-order conditional statistics of the baud-rate sampled received signal and the knowledge of the transmitted constellation. The main novelty of the proposed approach is that the receiver accomplishes fast channel-identification by using soft-statistics. In particular, the receiver consists of a symbol-by-symbol maximum a posteriori (SbS-MAP) detector that feeds a nonlinear Kalman-like channel estimator with the soft statistics constituted by the a posteriori probabilities (APPs) of the state sequence of the ISI channel. Several numerical results confirm that the proposed blind detector achieves the identification of nonminimum phase channels with deep spectral notches within 300 symbols, even at low signal-to-noise ratios (SNRs). Furthermore, an attractive feature of the proposed blind channel estimator is that it directly estimates the discrete-time impulse response of the unknown channel so that, in principle, any equalization technique for known channels may be performed after channel identification has been achieved     

Decision-feedback MAP receiver for time-selective fading CDMAchannels

A decision-feedback maximum a posteriori (MAP) receiver is proposed for code-division multiple-access channels with time-selective fading. The receiver consists of a sequence-matched filter and a MAP demodulator. Output samples (more than one per symbol) from the matched filter are fed into the MAP demodulator. The MAP demodulator exploits the channel memory by delaying the decision and using a sequence of observations. This receiver also rejects multiple-access interference and estimates channel fading coefficients implicitly to give good demodulation decisions. Moreover, computer simulations are performed to evaluate the bit-error rate performance of the receiver under various channel conditions     

Space-time coding ambiguities in joint adaptive channel estimation and detection

This paper studies the error propagation effect that is caused by certain ambiguities in joint data detection-channel tracking algorithms for transmission diversity schemes. Here, we use a space-time (ST) receiver based on the maximum a posteriori (MAP) method that takes into account the channel estimation error assuming the unknown channel to have a given complex multivariate Gaussian probability density function (pdf) (i.e., a Ricean channel). The decision criterion that is expressed in quadratic form represents either a linear detector or a noncoherent-nonlinear detector in extreme cases. Then, the channel pdf for the next iteration is updated by estimates of the second-order statistics of the channel coefficients, and a very simple decision-directed adaptive algorithm is derived for adaptive channel estimation. The adaptive algorithm can efficiently track a fast Rayleigh fading channel and, as a result, achieves robust performance. However, the occurrence of two types of ambiguities initiated in deep fades result in error propagation. Some remedies called space-time ambiguity remedies (STARs) are proposed to prevent error propagation. A new time-varying space-time coding (TVST) scheme is suggested as a bandwidth-efficient method to combat the permutation ambiguity impairment. This coding scheme, in conjunction with a differential detector, can resolve the ambiguity problem.     

Adaptive MAP receiver via the EM algorithm and message passings for MIMO-OFDM mobile communications

This paper proposes two new types of maximum a posteriori probability (MAP) receivers for multiple-input-multiple-output and orthogonal frequency-division multiplexing mobile communications with a channel coding such as the low-density parity-check code. One proposed receiver employs the expectation-maximization algorithm so as to improve performance of approximated MAP detection. Differently from a conventional receiver employing the minimum mean-square estimation (MMSE) algorithm, it applies the recursive least squares (RLS) algorithm to the channel estimation in order to track a fast fading channel. For the purpose of further improvement, the other proposed receiver applies a new adaptive algorithm that can be derived from the message passing on factor graphs. The algorithm exploits all detected signals but one of targeted time, and can gain a considerable advantage over the MMSE and RLS. Computer simulations show that the first proposed receiver is superior in channel-tracking ability to the conventional receiver employing the MMSE. Furthermore, it is demonstrated that the second proposed receiver remarkably outperforms both the conventional and the first proposed ones.     

A MAP Estimator for the m Parameter in Nakagami Fading Ultra-Wide Bandwidth Indoor Channels

A maximum a posteriori (MAP) estimator for the Nakagami m parameter in an ultra-wide bandwidth (UWB) indoor channel is proposed. Previous work exclusively studies maximum likelihood (ML) estimation and moment method (MM) estimation of the Nakagami m parameter. This letter derives the MAP estimator for the Nakagami m parameter by using the a priori probabilities of the Nakagami fading parameters in an indoor UWB channel. The performance of the MAP estimator is examined and compared with those of the ML estimator and the MM estimator. Numerical results demonstrate that the new MAP estimator is superior to the ML estimator and the MM estimator in an indoor UWB channel, especially when the sample size in the estimation is small     

Adaptive MAPSD algorithms for symbol and timing recovery of mobileradio TDMA signals

Dual-mode adaptive algorithms with rapid convergence properties are presented for the equalization of frequency selective fading channels and the recovery of time-division multiple access (TDMA) mobile radio signals. The dual-mode structure consists of an auxiliary adaptive filter that estimates the channel during the training cycle. The converged filter weights are used to initialize a parallel bank of filters that are adapted blindly during the data cycle. When the symbol timing is known, this filter bank generates error residuals that are used to perform approximate maximum a posteriori symbol detection (MAPSD) and provide reliable decisions of the transmitted signal. For channels with timing jitter, joint estimation of the channel parameters and the symbol timing using an extended Kalman filter algorithm is proposed. Various methods are described to reduce the computational complexity of the MAP detector, usually at the cost of some performance degradation. Also, a blind MAPSD algorithm for combining signals from spatially diverse receivers is derived. This diversity MAPSD (DMAPSD) algorithm, which can be easily modified for the dual-mode TDMA application, maintains a global set of MAP metrics even while blindly tracking the individual spatial channels using local error estimates. The performance of these single-channel and diversity MAPSD dual-mode algorithms are studied via computer simulations for various channel models, including a mobile radio channel simulator for the IS-54 digital cellular TDMA standard     

Turbo equalization with sequential sequence estimation overmultipath fading channels

We investigate the performance of a turbo equalization scheme over frequency-selective fading channels, where a soft-output sequential algorithm is employed as the estimation algorithm. The advantage of this scheme comes from the low computational complexity of the sequential algorithm, which is only linearly dependent on the channel memory length. Simulation results of an 8-PSK trellis-coded modulation (TCM) system show that the performance of this scheme suffers approximately 2-dB loss compared with that of the turbo max-log maximum a posteriori (MAP) probability equalizer after 5 iterations     

平衰落信道下ML和MAP信道估计与导频符号长度的关系性能比较

信道估计是无线通信系统必须加以解决的关键技术之一，采用导频符号辅助的方法进行信道估计是目前各类无线通信系统常用的方法。本文针对平衰落信道提出了最大似然(ML)算法和最大后验概率(MAP)估计算法，给出了ML估计和MAP估计之间的关系，仿真了MAP估计和ML估计的方差与导频符号长度的关系，提出当导频符号长度的取值超过20个符号长度时，MAP信道估计明显优于ML信道估计。     

基于奇异值分解的MIMO-OFDM系统角域MAP信道估计算法

最大后验概率信道估计算法应用于多输入多输出-正交频分复用(MIMOOFDM)系统时需要大规模的矩阵求逆和乘积运算,且系统数据传输效率随发送天线数的增加明显降低.为克服这些问题,提出了一种基于奇异值分解的角域最大后验概率信道估计算法.该算法通过期望最大化把(MIMO)信道估计问题简化为一系列独立的单输入单输出(SISO)问题,并使用奇异值分解避免了大规模矩阵求逆和乘积运算;通过多个OFDM符号联合估计信道提高了系统数据传输效率及算法的估计性能.仿真实验验证了此算法的有效性.     

Progressive transmission of images using MAP detection overchannels with memory

We propose a new maximum a posteriori (MAP) detector, without the need for explicit channel coding, to lessen the impact of communication channel errors on compressed image sources. The MAP detector exploits the spatial correlation in the compressed bitstream as well as the temporal memory in the channel to correct channel errors. We first present a technique for computing the residual redundancy inherent in a compressed grayscale image (compressed using VQ). The performance of the proposed MAP detector is compared to that of a memoryless MAP detector. We also investigate the dependence of the performance on memory characteristics of the Gilbert-Elliott channel as well as average channel error rate. Finally, we study the robustness of the proposed MAP detector's performance to estimation errors.     

Decision-directed estimation of MIMO time-varying Rayleigh fading channels

This paper presents a decision-directed (DD) maximum a posteriori probability (MAP) channel-estimation scheme for multiple-input multiple-output (MIMO) time-varying fading channels. With the estimate of the channel matrix for the current symbol interval, a zero-forcing (ZF) receiver is applied to detect the spatially multiplexed data on a symbol-by-symbol basis. Symbol decisions are then fed to the channel predictor for estimation of channel coefficients in future symbol intervals. Simulated error performance of a ZF receiver with the DD MAP and perfect channel estimates is provided and compared.     

Adaptive Bayesian and EM-based detectors for frequency-selective fading channels

The problems of adaptive maximum a posteriori (MAP) symbol detection for uncoded transmission and of adaptive soft-input soft-output (SISO) demodulation for coded transmission of data symbols over time-varying frequency-selective channels are explored within the framework of the expectation-maximization (EM) algorithm. In particular, several recursive forms of the classical Baum-Welch (BW) algorithm and its Bayesian counterpart (often referred to a Bayesian EM algorithm) are derived in an unified way. In contrast to earlier developments of the BW and BEM algorithms, these formulations lead to computationally attractive algorithms which avoid matrix inversions while using sequential processing over the time and trellis branch indices. Moreover, it is shown how these recursive versions of the BW and BEM algorithms can be integrated with the well-known forward-backward processing SISO algorithms resulting in adaptive SISOs with embedded soft decision directed (SDD) channel estimators. An application of the proposed algorithms to iterative "turbo-processing" receivers illustrates how these SDD channel estimators can efficiently exploit the extrinsic information obtained as feedback from the SISO decoder in order to enhance their estimation accuracy.     

Low-complexity iterative channel estimation for serially concatenated systems over frequency-nonselective Rayleigh fading channels

A low-complexity iterative maximum a posteriori (MAP) channel estimator is proposed whose complexity increases linearly with the symbol alphabet size 'M. Prediction-based MAP channel estimation is not appropriate with a high-order prediction filter or a large modulation alphabet size, since the computational complexity increases with M^L , where L is the predictor order. In contrast, the proposed channel estimator has a constant number of trellis states regardless of the prediction filter order, and is shown to provide comparable error performance to the prediction-based MAP estimator