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34
Adaptive joint detection and decoding in flatfading channels via mixture Kalman filtering
 IEEE Trans. Inf. Theory
, 2000
"... Abstract—A novel adaptive Bayesian receiver for signal detection and decoding in fading channels with known channel statistics is developed; it is based on the sequential Monte Carlo methodology that recently emerged in the field of statistics. The basic idea is to treat the transmitted signals as “ ..."
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Cited by 38 (6 self)
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Abstract—A novel adaptive Bayesian receiver for signal detection and decoding in fading channels with known channel statistics is developed; it is based on the sequential Monte Carlo methodology that recently emerged in the field of statistics. The basic idea is to treat the transmitted signals as “missing data ” and to sequentially impute multiple samples of them based on the observed signals. The imputed signal sequences, together with their importance weights, provide a way to approximate the Bayesian estimate of the transmitted signals and the channel states. Adaptive receiver algorithms for both uncoded and convolutionally coded systems are developed. The proposed techniques can easily handle the nonGaussian ambient channel noise. It is shown through simulations that the proposed sequential Monte Carlo receivers achieve nearbound performance in fading channels for both uncoded and coded systems, without the use of any training/pilot symbols or decision feedback. Moreover, the proposed receiver structure exhibits massive parallelism and is ideally suited for highspeed parallel implementation using the very large scale integration (VLSI) systolic array technology. Index Terms—Adaptive decoding, adaptive detection, coded system, flatfading channel, mixture Kalman filter, nonGaussian noise, sequential Monte Carlo methods. I.
On LDPC codes over channels with memory
 IEEE Trans. Wireless Commun
, 2006
"... Abstract — The problem of detection and decoding of lowdensity paritycheck (LDPC) codes transmitted over channels with memory is addressed. A new general method to build a factor graph which takes into account both the code constraints and the channel behavior is proposed and the a posteriori proba ..."
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Cited by 18 (12 self)
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Abstract — The problem of detection and decoding of lowdensity paritycheck (LDPC) codes transmitted over channels with memory is addressed. A new general method to build a factor graph which takes into account both the code constraints and the channel behavior is proposed and the a posteriori probabilities of the information symbols, necessary to implement maximum a posteriori (MAP) symbol detection, are derived by using the sumproduct algorithm. With respect to the case of a LDPC code transmitted on a memoryless channel, the derived factor graphs have additional factor nodes taking into account the channel behavior and not the code constraints. It is shown that the function associated to the generic factor node modeling the channel is related to the basic branch metric used in the Viterbi algorithm when MAP sequence detection is applied or in the BCJR algorithm implementing MAP symbol detection. This fact suggests that all the previously proposed solutions for those algorithms can be systematically extended to LDPC codes and graphbased detection. When the sumproduct algorithm works on the derived factor graphs, the most demanding computation is in general that performed at factor nodes modeling the channel. In fact, the complexity of the computation at these factor nodes is in general exponential in a suitably defined channel memory parameter. In these cases, a technique for complexity reduction is illustrated. In some particular cases of practical relevance, the above mentioned complexity becomes linear in the channel memory. This does not happen in the same cases when detection is performed by using the Viterbi algorithm or the BCJR algorithm, suggesting that the use of factor graphs and the sumproduct algorithm might be computationally more appealing. As an example of application of the described framework, the cases of noncoherent and flat fading channels are considered. Index Terms — Factor graphs, sumproduct algorithm, channels with memory, phasenoise, flat fading, lowdensity paritycheck codes, iterative detection/decoding. I.
Optimal Joint Detection/Estimation in Fading Channels with Polynomial Complexity
 IEEE Trans. Inform. Theory
, 2003
"... The problem of sequence detection in frequencynonselective/timeselective fading channels, when channel state information (CSI) is not available at the transmitter and receiver, is considered in this paper. The traditional belief is that exact maximum likelihood sequence detection (MLSD) of an ..."
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Cited by 11 (1 self)
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The problem of sequence detection in frequencynonselective/timeselective fading channels, when channel state information (CSI) is not available at the transmitter and receiver, is considered in this paper. The traditional belief is that exact maximum likelihood sequence detection (MLSD) of an uncoded sequence over this channel has exponential complexity in the channel coherence time.
A unified framework for finitememory detection
 IEEE J. SAC
, 2005
"... In this paper, we present a general approach to finitememory detection. From a semitutorial perspective, a number of previous results are rederived and new insights are gained within a unified framework. A probabilistic derivation of the wellknown Viterbi algorithm (VA), forwardbackward (FB), an ..."
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Cited by 10 (8 self)
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In this paper, we present a general approach to finitememory detection. From a semitutorial perspective, a number of previous results are rederived and new insights are gained within a unified framework. A probabilistic derivation of the wellknown Viterbi algorithm (VA), forwardbackward (FB), and sumproduct (SP) algorithms, shows that a basic metric emerges naturally under very general causality and finitememory conditions. This result implies that detection solutions based on one algorithm can be systematically extended to other algorithms. For stochastic channels described by a suitable parametric model, a conditional Markov property is shown to imply this finitememory condition. Unfortunately, this property is seldom met in practice and optimality cannot be claimed. We show, however, that in the case of transmission over a linear channel characterized by a single timeinvariant stochastic parameter, a finitememory detection strategy is asymptotically optimal, regardless of the particular algorithm used (VA, FB, or SP). We consider, as examples, linear predictive and noncoherent detection schemes. The final conclusion is that while asymptotic optimality for increasing complexity can often be achieved, key issues in the design of detection algorithms are the computational efficiency and the performance for limited complexity. Index Terms MAP sequence/symbol detection, iterative detection, graphbased detection, adaptive detection, finitememory detection, Viterbi algorithm, forwardbackward algorithm, sumproduct algorithm.
WaveletBased Sequential Monte Carlo Blind Receivers In Fading Channels with Unknowns Channel Statistics
 IEEE Trans. Sig. Proc
, 2004
"... Recently, an adaptive Bayesian receiver for blind detection in flatfading channels was developed by the present authors, based on the sequential Monte Carlo methodology. That work is built on a parametric modeling of the fading process in the form of a statespace model and assumes the knowledge of ..."
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Cited by 8 (4 self)
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Recently, an adaptive Bayesian receiver for blind detection in flatfading channels was developed by the present authors, based on the sequential Monte Carlo methodology. That work is built on a parametric modeling of the fading process in the form of a statespace model and assumes the knowledge of the secondorder statistics of the fading channel. In this paper, we develop a nonparametric approach to the problem of blind detection in fading channels, without assuming any knowledge of the channel statistics. The basic idea is to decompose the fading process using a wavelet basis and to use the sequential Monte Carlo technique to track both the wavelet coefficients and the transmitted symbols. A novel resamplingbased wavelet shrinkage technique is proposed to dynamically choose the number of wavelet coefficients to best fit the fading process. Under such a framework, blind detectors for both flatfading channels and frequencyselective fading channels are developed. Simulation results are provided to demonstrate the excellent performance of the proposed blind adaptive receivers.
Expectation Propagation for Signal Detection in FlatFading Channels
 Proceedings of IEEE International Symposium on Information Theory
, 2003
"... In this paper, we propose a new Bayesian receiver for signal detection in flatfading channels. ..."
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Cited by 7 (4 self)
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In this paper, we propose a new Bayesian receiver for signal detection in flatfading channels.
Iterative Detection for Channels With Memory
, 2007
"... In this paper, we present an overview on the design of algorithms for iterative detection over channels with memory. The starting point for all the algorithms is the implementation of softinput softouput maximum a posteriori (MAP) symbol detection strategies for transmissions over channels encom ..."
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Cited by 4 (1 self)
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In this paper, we present an overview on the design of algorithms for iterative detection over channels with memory. The starting point for all the algorithms is the implementation of softinput softouput maximum a posteriori (MAP) symbol detection strategies for transmissions over channels encompassing unknown parameters, either stochastic or deterministic. The proposed solutions represent effective ways to reach this goal. The described algorithms are grouped into three categories: i) we first introduce algorithms for adaptive iterative detection, where the unknown channel parameters are explicitly estimated; ii) then, we consider finitememory iterative detection algorithms, based on ad hoc truncation of the channel memory and often interpretable as based on an implicit estimation of the channel parameters; and iii) finally, we present a general detectiontheoretic approach to derive optimal detection algorithms with polynomial complexity. A few illustrative numerical results are also presented.
On trellisbased truncatedmemory detection
 IEEE Trans. Commun
, 2005
"... Abstract—We propose a general framework for trellisbased detection over channels with infinite memory. A general truncation assumption enables the definition of a trellis diagram, which takes into account a considered portion of the channel memory and possible coding memory at the transmitter side. ..."
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Cited by 3 (2 self)
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Abstract—We propose a general framework for trellisbased detection over channels with infinite memory. A general truncation assumption enables the definition of a trellis diagram, which takes into account a considered portion of the channel memory and possible coding memory at the transmitter side. It is shown that trellisbased maximum a posteriori (MAP) symbol detection algorithms, in the form of forwardbackward (FB) algorithms, can be derived on the basis of this memorytruncation assumption. A general approach to the design of truncatedmemory (TM) FB algorithms is proposed, and two main classes of algorithms, characterized by coupled and decoupled recursions, respectively, are presented. The complexity of the derived TMFB algorithms is analyzed in detail. Moreover, it is shown that MAP sequence detection algorithms, based on the Viterbi algorithm, follow easily from one of the proposed classes. Looking backward at this duality between MAP symbol detection algorithms and MAP sequence detection algorithms, it is shown that previous solutions for one case can be systematically extended to the other case. The generality of the proposed framework is shown by considering various examples of stochastic channels. New detection algorithms, as well as generalizations of solutions previously published in the literature, are embedded in the proposed framework. The obtained results do suggest that the performance of the proposed detection algorithms ultimately depends on the truncation depth, almost regardless of the specific detection strategy. Index Terms—Forwardbackward (FB) algorithm, iterative detection, maximum a posteriori (MAP) sequence/symbol detection, memory truncation, trellisbased detection, Viterbi algorithm (VA). I.
On the ARMA Approximation for Fading Channels Described by the Clarke Model with Applications to Kalmanbased Receivers
"... Abstract—We consider a terrestrial wireless channel, whose statistical model under flatfading conditions is due to Clarke. A lot of papers in the literature deal with receivers for this scenario, aiming at estimating and tracking the timevarying channel, possibly with the aid of known (pilot) symb ..."
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Cited by 2 (0 self)
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Abstract—We consider a terrestrial wireless channel, whose statistical model under flatfading conditions is due to Clarke. A lot of papers in the literature deal with receivers for this scenario, aiming at estimating and tracking the timevarying channel, possibly with the aid of known (pilot) symbols. A common approach to derive receivers of reasonable complexity is to resort to a Kalman filter which is based on an approximation of the actual fading process as autoregressive movingaverage (ARMA) of a given order. The aim of this paper is to show that the approximation of the actual fading process, usually exploited in the literature, is far from effective. Thus, we present a novel technique, based on an offline minimization of the mean square error of the channel estimate, which ensures a considerable gain in terms of biterror rate for Kalmanbased receivers without increasing the receiver complexity. Moreover, we also propose a novel approximation, to be employed in Kalman smoothers proposed for iterative detection schemes, which allows further performance improvements without a significant increase of the computational complexity. Index Terms—Fading channels, timevarying channels, parameter estimation, autoregressive moving average processes, Wiener filtering, Kalman filtering. I.
Multisampling decisionfeedback linear prediction receivers for differential space–time modulation over Rayleigh fast fading channels
 IEEE Trans. Commun
, 2003
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