Combining Decoded-and-Forwarded Signals in Gaussian Cooperative Channels

Djeumou, Brice; Lasaulce, Samson; Klein, Andrew G.

doi:10.1109/isspit.2006.270875

Cited by 14 publications

(26 citation statements)

References 10 publications

(10 reference statements)

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“…Thus, the complexity of the proposed scheme increases as the modulation increases. However, to assist the detection at the destination, our proposed scheme only requires the average receive SNR of S-R link to compute individual SER of the modulation as shown in (6) and (7). Therefore, our proposed scheme still inherits an interesting trade-off between the error rate performance and the system complexity.…”

Section: Complexity Comparisonmentioning

confidence: 99%

“…In [6], Cooperative-MRC (C-MRC) is introduced and yet, it suffers losses in assymetrical networks and is not easily feasible in relay networks with different modulations. In [7]- [8], the authors have presented a combining strategy with perfect channel state information (CSI) of S-R link at the destination. However, their work assumes average symbol error rate (SER) as the side information and hence, this strategy does not offer a complete ML solution.…”

Section: Introductionmentioning

confidence: 99%

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Maximum likelihood combining in cooperative relay networks with different modulations

Aziz

Iwanami

2011

Ictc 2011

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This paper proposes a novel combining strategy with different modulations for cooperative relay networks. We model a Maximum Likelihood (ML) detection at the destination with higher order modulations such as quadrature amplitude modulation (QAM) which accounts individual symbol error rate (SER) to facilitate the detection. Our proposed algorithm is flexible to signals with different modulations as detection is done symbol-bysymbol basis. If different modulations are used at the source and the relays, we propose that lower modulation is used at the source. By computer simulations, significant Packet Error Rate (PER) performance can be obtained by the proposed scheme against Cooperative-Maximum Ratio Combining (C-MRC).

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Section: Complexity Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Maximum likelihood combining in cooperative relay networks with different modulations

Aziz

Iwanami

2011

Ictc 2011

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show abstract

“…In this case, the linear combiners derived by [3,4,6] cannot be used in general. However, provided that the above compatibility condition is met, the ML combiner can be derived as we show now.…”

Section: When the Source And Relay Use Arbitrary And Different Modulamentioning

confidence: 99%

“…Even if the modulations at the source and relay are identical, the MRC can severely degrade the receiver performance because it does not compensate for the decoding noise introduced by the relay [2][3][4][5][6]. This is why the authors of [2,4] proposed a maximum-likelihood detector (MLD) for combining two BPSK-modulated signals coming from the source and relay. The authors of [6] proposed an improved MRC called C-MRC which aims at maximizing receive diversity.…”

mentioning

confidence: 99%

Combining Coded Signals with Arbitrary Modulations in Orthogonal Relay Channels

Djeumou

Lasaulce

Berthet

2008

Journal of Electrical and Computer Engineering

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We consider a relay channel for which the following assumptions are made. (1) The source-destination and relay-destination channels are orthogonal (frequency division relay channel). (2) The relay implements the decode-and-forward protocol. (3) The source and relay implement the same channel encoder, namely, a convolutional encoder. (4) They can use arbitrary and possibly different modulations. In this framework, we derive the best combiner in the sense of the maximum likelihood (ML) at the destination and the branch metrics of the trellis associated with its channel decoder for the ML combiner and also for the maximum ratio combiner (MRC), cooperative-MRC (C-MRC), and the minimum mean-square error (MMSE) combiner.

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“…However, C-MRC results in serious propagation error under asymmetrical networks when SNR of R-D link is larger than that of S-D link or S-R link. In addition, unlike ML-based strategy, C-MRC cannot be used in relay networks with arbitrary modulation (Djeumou et al, 2006). Djeumou et al (2006) proposed the performance of the hard-decision ML criterion detectionbased combining technique under coded cooperative scheme.…”

Section: Introductionmentioning

confidence: 99%

Maximum likelihood detection with arbitrary modulations in cooperative relay channels

Aziz

Iwanami

2011

IJWMC

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This paper proposes a simple combining technique with arbitrary modulations for a cooperative relay scheme based on a detect-and-forward relay protocol. Here we present maximum likelihood criterion detection at the destination which considers individual symbol error rate to facilitate the detection in noisy relay networks. In particular, our proposed algorithm is flexible to signals with different modulation formats as detection is done on a symbol-bysymbol basis. If different modulations are used at the source and the relays, we propose that lower modulation constellation is used at the source. By computer simulations, the results show that significant packet error rate performance can be achieved by the proposed scheme and we compare it against cooperative-maximum ratio combining and selection combining.

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Combining Decoded-and-Forwarded Signals in Gaussian Cooperative Channels

Abstract: -The

Cited by 14 publications

References 10 publications

Maximum likelihood combining in cooperative relay networks with different modulations

Maximum likelihood combining in cooperative relay networks with different modulations

Combining Coded Signals with Arbitrary Modulations in Orthogonal Relay Channels

Maximum likelihood detection with arbitrary modulations in cooperative relay channels

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