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53
Turbo DPSK: Iterative differential PSK demodulation and channel decoding
 In Proc. IEEE International Symp. on Information Theory (ISIT
, 1999
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Cited by 70 (0 self)
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Personal use of this material is permitted. However, permission to reprint/ republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component
Joint Map Equalization And Channel Estimation For FrequencySelective FastFading Channels
, 1998
"... This paper presents a maximum a posteriori (MAP) equalizer for bandlimited signals on frequency selective fading channels. A key contribution is the way in which the statespace of the MAP trellis is expanded for the purpose of joint channel estimation and equalization. The fading channel is estimate ..."
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Cited by 41 (9 self)
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This paper presents a maximum a posteriori (MAP) equalizer for bandlimited signals on frequency selective fading channels. A key contribution is the way in which the statespace of the MAP trellis is expanded for the purpose of joint channel estimation and equalization. The fading channel is estimated by coupling minimum mean square error techniques with the expanded MAP trellis. The symbolbysymbol MAP algorithm is a softinput softoutput technique. The new MAP receiver can also be applied to iterative
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.
DecisionFeedback Differential Detection of MDPSK for Flat Rayleigh Fading Channels
 IEEE TRANS. ON COMMUN
, 1999
"... In this paper, a novel decisionfeedback differential detection (DF{DD) scheme for 16level differentially encoded amplitude phase shift keying (16 DAPSK) is proposed. It is shown that the new technique based on multiplesymbol detection (MSD) [1] may obtain a significant gain in power efficiency un ..."
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Cited by 28 (5 self)
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In this paper, a novel decisionfeedback differential detection (DF{DD) scheme for 16level differentially encoded amplitude phase shift keying (16 DAPSK) is proposed. It is shown that the new technique based on multiplesymbol detection (MSD) [1] may obtain a significant gain in power efficiency under additive white Gaussian noise (AWGN) conditions when compared with a previously proposed DFDD scheme [2]. This gain increases with decreasing number of feedback symbols, which makes the novel scheme attractive for implementation since DFDD schemes are the more robust against frequency offset the less feedback symbols are applied. In addition, a recursive version of the DFDD scheme is derived which allows to find out easily the best tradeoff between power efficiency under AWGN conditions and robustness against frequency offset via adjustment of a forgetting factor.
Multiple–Symbol Differential Sphere Decoding
 IEEE Trans. Commun
, 2005
"... Abstract — In multiplesymbol differential detection (MSDD) for powerefficient transmission over Rayleigh fading channels without channel state information, blocks of N received symbols are jointly processed to decide on N1 data symbols. The search space for the maximumlikelihood (ML) estimate i ..."
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Cited by 25 (6 self)
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Abstract — In multiplesymbol differential detection (MSDD) for powerefficient transmission over Rayleigh fading channels without channel state information, blocks of N received symbols are jointly processed to decide on N1 data symbols. The search space for the maximumlikelihood (ML) estimate is therefore (complex) (N1)dimensional, and maximumlikelihood MSDD (MLMSDD) quickly becomes computationally intractable as N grows. Mackenthun’s lowcomplexity MSDD algorithm finds the ML estimate only for Rayleigh fading channels that are timeinvariant over an N symbol period. For the general timevarying fading case, however, lowcomplexity MLMSDD is an unsolved problem. In this letter, we solve this problem by applying sphere decoding (SD) to MLMSDD for timevarying Rayleigh fading channels. The resulting technique is referred to as multiplesymbol differential sphere decoding (MSDSD). Index terms: Multiplesymbol differential detection, Sphere decoding, MaximumLikelihood decoding, Rayleigh fading channels
Channel Estimation with Superimposed Pilot Sequence
 in Proc. Advanced Signal Processing for Communications Symposium in conjunction with IEEE GLOBECOM '99, Rio de Janeiro
, 1999
"... For the purpose of various synchronization tasks (including carrier phase, time, frequency, and frame synchronization) , one may add a known pilot sequence, typically a pseudonoise sequence, to the unknown data sequence. This approach is known as a spreadspectrum pilot technique or as a superimpos ..."
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Cited by 24 (1 self)
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For the purpose of various synchronization tasks (including carrier phase, time, frequency, and frame synchronization) , one may add a known pilot sequence, typically a pseudonoise sequence, to the unknown data sequence. This approach is known as a spreadspectrum pilot technique or as a superimposed pilot sequence technique.
Capacity, mutual information, and coding for finitestate Markov channels
 IEEE Trans. Inform. Theory
, 1996
"... Abstract The FiniteState Markov Channel (FSMC) is a discretetime varying channel whose variation is determined by a finitestate Markov process. These channels have memory due to the Markov channel variation. We obtain the FSMC capacity as a function of the conditional channel state probability. W ..."
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Cited by 15 (1 self)
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Abstract The FiniteState Markov Channel (FSMC) is a discretetime varying channel whose variation is determined by a finitestate Markov process. These channels have memory due to the Markov channel variation. We obtain the FSMC capacity as a function of the conditional channel state probability. We also show that for i.i.d. channel inputs, this conditional probability converges weakly, and the channel's mutual information is then a closedform continuous function of the input distribution. We next consider coding for FSMCs. In general, the complexity of maximumlikelihood decoding grows exponentially with the channel memory length. Therefore, in practice, interleaving and memoryless channel codes are used. This technique results in some performance loss relative to the inherent capacity of channels with memory. We propose a maximumlikelihood decisionfeedback decoder with complexity that is independent of the channel memory. We calculate the capacity and cutoff rate of our technique, and show that it preserves the capacity of certain FSMCs. We also compare the performance of the decisionfeedback decoder with that of interleaving and memoryless channel coding on a fading channel with 4PSK modulation.
Adaptive Bayesian and EMbased detectors for frequencyselective fading channels
 IEEE Transactions on Comm
, 2003
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Particle Filtering for Demodulation in Fading Channels with NonGaussian Additive Noise.
, 2001
"... In this paper, an ecient particle ltering algorithm is developed to solve the problem of demodulation of Mary modulated signals under conditions of fading channels in the presence of nonGaussian additive noise. Simulations for MDPSK signals are presented. The results show that the algorithm outper ..."
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Cited by 12 (2 self)
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In this paper, an ecient particle ltering algorithm is developed to solve the problem of demodulation of Mary modulated signals under conditions of fading channels in the presence of nonGaussian additive noise. Simulations for MDPSK signals are presented. The results show that the algorithm outperforms the current methods. E. Punskaya and A. Doucet are sponsored by EPSRC, UK. y C. Andrieu is sponsored by AT&T Lab, Cambridge, UK. 1 I. Introduction The problem of recovering a message coded as a sequence of symbols and passed through a transmission channel is of great interest in digital communications. This optimal ltering problem has proved to be especially dicult under conditions of fading transmission channels. Several suboptimal schemes have been proposed to solve it, see for example [3], [5], [6]. This paper presents an original particle ltering algorithm to obtain the estimates of the posterior distribution of the symbols in the case of Mary modulated signals under ...
An HMM approach to adaptive demodulation of QAM signals in fading channels
 Int. Jour. of Adaptive Control and Signal Processing
, 1994
"... In this paper the techniques of extended Kalman filtering (EKF) and hidden Markov mode! (HMM) signal processing are combined to adaptively demodulate quadrature amplitudemodulated (QAM) signals in noisy fading channels. This HMM approach is particularly suited to signals for which the message symbo ..."
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Cited by 11 (8 self)
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In this paper the techniques of extended Kalman filtering (EKF) and hidden Markov mode! (HMM) signal processing are combined to adaptively demodulate quadrature amplitudemodulated (QAM) signals in noisy fading channels. This HMM approach is particularly suited to signals for which the message symbols are not equally probable, as is the case with many types of coded signals. Our approach is to formulate the QAM signal by a finitediscrete state process and represent the channel model by a continuous state process. The mixed state model is then reformulated in terms of conditional information states using HMM theory. This leads to models which are amenable to standard EKF or related techniques. A sophisticated EKF scheme with an HMM subfilter is discussed, as well as more practical schemes coupling discrete state HMM filters and continuous state Kalman filters. The case of white noise is considered, as well as generalizations to cope with coloured noise. Simulation studies demonstrate the improvement gained over standard schemes.