Near-optimal symbol-by-symbol detection schemes for flat Rayleigh fading

Seymour, James P.; Fitz, M.P.

doi:10.1109/ctmc.1994.512569

Cited by 5 publications

(12 citation statements)

References 21 publications

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“…It has states, . The computation in (10) may be distributed over the trellis as the product of a start metric , the branch metrics , and an end metric up to a factor , as (12) (13) 14from (10). Whenever or are inconsistent with the training bits, is set to zero.…”

Section: Trellis Representationmentioning

confidence: 99%

“…However, although there is rich literature on MLSDs for time-varying frequency-selective channels [4], [5], [8]- [11], the MAP research is less complete [6], [12]- [15]. In this unified paper, we provide a novel performance analysis of MAPDs in time-varying channels, and we coherently and comprehensively address the following practically important topics found also in the MLSD literature: linear modulations, time-varying frequency-selective channels, differential phase detection, fractional sampling, and detection when the channel impulse response (CIR) is known (the known CIR MAPDs [8]) or averaged over (the predictor MAPDs [9], [11]).…”

Section: Introductionmentioning

confidence: 99%

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Innovations-based MAP detection for time-varying frequency-selective channels

Hart

Pasupathy

2000

IEEE Trans. Commun.

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By using the innovations process, this paper provides a unification and extension of the existing maximum a posteriori (MAP) detectors (MAPDs). The practically important topics of linear modulations, time-varying frequency-selective channels, differential phase detection, and fractional sampling are accounted for. The MAPDs are derived under different conditions of optimality and a priori knowledge as follows: when the MAP criterion is applied to the constellation mapper's input bits or output symbols, when all observations or only a fixed number of future observations (i.e., fixed-lag MAPDs) from a transmission are available, when the time-varying channel impulse response is perfectly known, and when only the Gaussian-distributed channel's mean and autocovariance and the noise variance are known. As these quantities are actually unknown, their estimation in the context of MAP detection is also discussed. The MAPDs are characterized through simulation and a novel, unified analysis. Although MAPDs are less suited to hardware implementation than the traditional maximum-likelihood sequence detectors, the MAPDs can accept nonuniform a priori bit or symbol probabilities and provide soft outputs. In this way, the MAPDs are well suited to iterative decoding, and so they will become increasing integral to high-performance receiver designs. Index Terms-Dispersive channels, error analysis, MAP symbol detection, Rayleigh channels, Rician channels, time-varying channels. Paper approved by O. Andrisano, the Editor for Modulation for Fading Channels of the IEEE Communications Society.

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Section: Trellis Representationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Innovations-based MAP detection for time-varying frequency-selective channels

Hart

Pasupathy

2000

IEEE Trans. Commun.

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“…Another advantage is that, the symbol APP inforniation provided by previous transmission can be stored in memory and be used to further reduce decoding complexity of next retransmission. This efficient trellis pruning based on the T-algorithm has proven very efficient in Rayleigh fading channels [13]. The application of the OSA in this environment has two slight twists that should be explored: inclusion of pilot symbol assisted demodulation and branch metric computation accounting for the additional parity transmissions.…”

Section: A Osa F O R Rc-tcm With Pilot Symbolsmentioning

confidence: 99%

“…Consequently this quantity will feature proininently in any optimal or near optiiiial tleinodulator structure. Due to the coiiditionnl Gaussian iiatiirc of tlie channel aiid the noise this density caii be foriiiiilatcd as in [13]. This results in a trellis wliicli lias n size espuiiciitial in 1<,1 (a much larger trellis than for the code doiie).…”

Section: [ -mentioning

confidence: 99%

An efficient ARQ scheme using soft output demodulation in Rayleigh fading channel

Lou

Fitz

WCNC. 1999 IEEE Wireless Communications and Networking Conference (Cat. No.99TH8466)

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“…As such, traditional solutions to the problem of SbSSDM generation for time-selective flat fading channels also have exponential complexity with respect to the sequence length. 2 In fact, most of the sequence detection approaches can be adapted to this case (e.g., [6][7][8]). Alternatively, quantization of the infinite dimensional messages and approximate integration can be performed in the case of message passing algorithms [9].…”

Section: Introductionmentioning

confidence: 99%

Polynomial complexity ML sequence and symbol-by-symbol detection in fading channels

Motedayen

Anastasopoulos

IEEE International Conference on Communications, 2003. ICC '03.

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The related problems of maximum likelihood sequence detection (MLSD) and symbol-by-symbol soft-decision metric (SbSSDM) generation in complex Gaussian flat-fading channels are considered in this paper. Traditional methods for the exact solution of these problems have exponential complexity with respect to the sequence length. In this paper, it is shown that both these problems can be solved in polynomial complexity with respect to the sequence length. Furthermore, motivated by the polynomialcomplexity exact algorithm, an approximate fast algorithm is also derived. Simulation results for a low-density parity-check (LDPC) code transmitted on the aforementioned channel show that the performance of the approximate algorithm is very close to the exact sum-product algorithm.2718 0-7803-7802-4/03/$17.00

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Near-optimal symbol-by-symbol detection schemes for flat Rayleigh fading

Cited by 5 publications

References 21 publications

Innovations-based MAP detection for time-varying frequency-selective channels

Innovations-based MAP detection for time-varying frequency-selective channels

An efficient ARQ scheme using soft output demodulation in Rayleigh fading channel

Polynomial complexity ML sequence and symbol-by-symbol detection in fading channels

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