Maximum likelihood decoding of uncoded and coded PSK signal sequences transmitted over Rayleigh flat-fading channels

Vitetta, G.M.; Taylor, D.P.

doi:10.1109/26.481226

Cited by 98 publications

(51 citation statements)

References 25 publications

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“…Hence, for increasing values of C, the number of factor nodes increases linearly but the computational burden at each factor node remains the same. Hence, the algorithm complexity is linear in C. This is a fundamental difference with respect to trellis-based linear predictive receivers [42], [43], [71], [73], [75], whose complexity is exponential in the prediction order, and suggests that by using the tool represented by FGs and SPA new computationally efficient algorithms can be derived. In addition, in this modified FG there are no cycles of length 4 in the part of the graph modeling the channel and, by adopting the flooding schedule, the derived algorithm is also well suited for a fully parallel implementation of the detector/ decoder.…”

Section: ) Examplevequal Energy Signals Transmitted Over a Flatmentioning

confidence: 99%

“…where the order of Markovianity C can be interpreted as the prediction order, fp i g C i¼1 are the prediction coefficients (which depend on state S k , but not on symbol a k ), and 2 k represents the mean square prediction error at epoch k. The result in (56), which can be derived from (53) owing to the Gaussianity of the observable, was obtained in [42], [43], and [71]- [75] as a solution for maximum likelihood sequence detection over fading channels. Ì…”

Section: Finite-memory Iterative Detectionmentioning

confidence: 99%

“…A similar behavior is observed for different values of the normalized Doppler rate. In addition, due to the linear complexity of the detection algorithm, it is possible to implement receivers with values of C higher than those used in [42], [43], [71], [73], and [75] for Viterbi-or BCJR-based algorithms, thus closer approaching the performance of the receiver with perfect CSI.…”

Section: B Non-trellis-based Iterative Detectionmentioning

confidence: 99%

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Iterative Detection for Channels With Memory

et al. 2007

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By examining various approaches and techniques, this paper aims to help designers to create improved algorithms for communications over noisy channels with memory.By Achilleas Anastasopoulos, Member IEEE, Keith M. Chugg, Member IEEE, Giulio Colavolpe, Member IEEE, Gianluigi Ferrari, Member IEEE, and Riccardo Raheli, Member IEEE ABSTRACT | 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 soft-input soft-ouput 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 finite-memory 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 detection-theoretic approach to derive optimal detection algorithms with polynomial complexity. A few illustrative numerical results are also presented.

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Section: ) Examplevequal Energy Signals Transmitted Over a Flatmentioning

confidence: 99%

Section: Finite-memory Iterative Detectionmentioning

confidence: 99%

Section: B Non-trellis-based Iterative Detectionmentioning

confidence: 99%

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Iterative Detection for Channels With Memory

et al. 2007

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“…We observed that this can lead to numerical problems at high SNR, where the noise variance becomes small. To overcome this problem, one may increase the accuracy of the numerical integration techniques used to compute (13), or prevent the variances of the Gaussian pdfs to become too small and trigger numerical problems.…”

Section: B Multi-trellis Siso Algorithm For Fading Channelmentioning

confidence: 99%

Detection by multiple trellises

Franceschini

Ferrari

Raheli

2009

IEEE Trans. Commun.

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Abstract-In this paper, we present a novel pragmatic approach, referred to as detection by multiple trellises, to perform trellis-based detection over realistic channels. More precisely, we consider channels with unknown parameters and apply the concept of detection by multiple trellises to forward-backward (FB) algorithms. The key idea of our approach consists, first, of properly quantizing the channel parameters and, then, considering replication of coherent FB algorithms operating on parallel trellises, one per hypothetical quantized value. In order to make the receiver robust against a possibly time-varying channel parameters, the proposed soft-output algorithms perform a proper "manipulation" of the forward and backward metrics computed by the parallel FB algorithms at regularly spaced trellis steps. We consider two significant examples of application: detection over (i) phase-uncertain channels and (ii) fading channels. The performance of the proposed algorithms is investigated considering differentially encoded (DE) quaternary phase shift keying (QPSK) and iterative detection schemes based on low-density parity-check (LDPC) codes. Besides having a low complexity, the proposed soft-output algorithms turn out to be robust, flexible, blind, in the sense that no knowledge of the channel parameter statistics is required, and highly parallelizable, as it is desirable in high-throughput future wireless communication systems.

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“…The basic idea is to extend the observation length to be larger than two consecutive signaling intervals and to gain additional information among received signals to improve detection. In time-varying channels, these receivers are closely related to a linear prediction receiver (e.g., [9], [12], and [13]). The linear prediction receiver finds the best transmitted sequence that minimizes Euclidean distance between the channel gains formed by the received signals and those from the prediction.…”

Section: Introductionmentioning

confidence: 99%

Improved Approximate Maximum-Likelihood Receiver for Differential Space–Time Block Codes Over Rayleigh-Fading Channels

Tarasak

Minn

Bhargava

2004

IEEE Trans. Veh. Technol.

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Abstract-In this paper, an approximate maximum-likelihood (ML) receiver for differential space-time block codes is investigated. The receiver is derived from the ML criterion and is shown to mitigate error floor occurring in a conventional differential receiver very well. Because the receiver employs knowledges of signal-to-noise ratio (SNR) and fading rate, we study mismatched cases when these parameters are not accurate. It is shown that the receiver is more sensitive to the mismatched parameters when the fading rate is high. Then, a union bound on the bit error probability is derived. The bounds show good agreement with the simulation results at high fading rate and at high SNR. Finally, a modified receiver, denoted as multistage receiver, is proposed to compensate the so-called intrablock interference caused by the time-varying characteristic of the channel within a transmission block. The multistage receiver offers further reduction of error floor of about half order of magnitude as compared with an approximate ML receiver.Index Terms-Bit error probability, differential space-time block codes, maximum-likelihood receiver.

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Maximum likelihood decoding of uncoded and coded PSK signal sequences transmitted over Rayleigh flat-fading channels

Cited by 98 publications

References 25 publications

Iterative Detection for Channels With Memory

Iterative Detection for Channels With Memory

Detection by multiple trellises

Improved Approximate Maximum-Likelihood Receiver for Differential Space–Time Block Codes Over Rayleigh-Fading Channels

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