Diversity Analysis of MIMO Decode-and-Forward Relay Network by Using Near-ML Decoder

Xianglan JIN; Dong-Sup JIN; Jong-Seon NO; Dong-Joon SHIN

doi:10.1587/transcom.E94.B.2828

Diversity Analysis of MIMO Decode-and-Forward Relay Network by Using Near-ML Decoder

Xianglan JIN, Dong-Sup JIN, Jong-Seon NO, Dong-Joon SHIN

Full Text Views

0

Share
Cite this

Summary :

The probability of making mistakes on the decoded signals at the relay has been used for the maximum-likelihood (ML) decision at the receiver in the decode-and-forward (DF) relay network. It is well known that deriving the probability is relatively easy for the uncoded single-antenna transmission with M-pulse amplitude modulation (PAM). However, in the multiplexing multiple-input multiple-output (MIMO) transmission, the multi-dimensional decision region is getting too complicated to derive the probability. In this paper, a high-performance near-ML decoder is devised by applying a well-known pairwise error probability (PEP) of two paired-signals at the relay in the MIMO DF relay network. It also proves that the near-ML decoder can achieve the maximum diversity of M_SM_D+M_R min (M_S,M_D), where M_S, M_R, and M_D are the number of antennas at the source, relay, and destination, respectively. The simulation results show that 1) the near-ML decoder achieves the diversity we derived and 2) the bit error probability of the near-ML decoder is almost the same as that of the ML decoder.

Publication: IEICE TRANSACTIONS on Communications Vol.E94-B No.10 pp.2828-2836

Publication Date: 2011/10/01

Publicized

Online ISSN: 1745-1345

DOI: 10.1587/transcom.E94.B.2828

Type of Manuscript: PAPER

Category: Wireless Communication Technologies

Cite this

Copy

Xianglan JIN, Dong-Sup JIN, Jong-Seon NO, Dong-Joon SHIN, "Diversity Analysis of MIMO Decode-and-Forward Relay Network by Using Near-ML Decoder" in IEICE TRANSACTIONS on Communications, vol. E94-B, no. 10, pp. 2828-2836, October 2011, doi: 10.1587/transcom.E94.B.2828.
Abstract: The probability of making mistakes on the decoded signals at the relay has been used for the maximum-likelihood (ML) decision at the receiver in the decode-and-forward (DF) relay network. It is well known that deriving the probability is relatively easy for the uncoded single-antenna transmission with M-pulse amplitude modulation (PAM). However, in the multiplexing multiple-input multiple-output (MIMO) transmission, the multi-dimensional decision region is getting too complicated to derive the probability. In this paper, a high-performance near-ML decoder is devised by applying a well-known pairwise error probability (PEP) of two paired-signals at the relay in the MIMO DF relay network. It also proves that the near-ML decoder can achieve the maximum diversity of M_SM_D+M_R min (M_S,M_D), where M_S, M_R, and M_D are the number of antennas at the source, relay, and destination, respectively. The simulation results show that 1) the near-ML decoder achieves the diversity we derived and 2) the bit error probability of the near-ML decoder is almost the same as that of the ML decoder.
URL: https://globals.ieice.org/en_transactions/communications/10.1587/transcom.E94.B.2828/_p

Copy

@ARTICLE{e94-b_10_2828,
author={Xianglan JIN, Dong-Sup JIN, Jong-Seon NO, Dong-Joon SHIN, },
journal={IEICE TRANSACTIONS on Communications},
title={Diversity Analysis of MIMO Decode-and-Forward Relay Network by Using Near-ML Decoder},
year={2011},
volume={E94-B},
number={10},
pages={2828-2836},
abstract={The probability of making mistakes on the decoded signals at the relay has been used for the maximum-likelihood (ML) decision at the receiver in the decode-and-forward (DF) relay network. It is well known that deriving the probability is relatively easy for the uncoded single-antenna transmission with M-pulse amplitude modulation (PAM). However, in the multiplexing multiple-input multiple-output (MIMO) transmission, the multi-dimensional decision region is getting too complicated to derive the probability. In this paper, a high-performance near-ML decoder is devised by applying a well-known pairwise error probability (PEP) of two paired-signals at the relay in the MIMO DF relay network. It also proves that the near-ML decoder can achieve the maximum diversity of M_SM_D+M_R min (M_S,M_D), where M_S, M_R, and M_D are the number of antennas at the source, relay, and destination, respectively. The simulation results show that 1) the near-ML decoder achieves the diversity we derived and 2) the bit error probability of the near-ML decoder is almost the same as that of the ML decoder.},
keywords={},
doi={10.1587/transcom.E94.B.2828},
ISSN={1745-1345},
month={October},}

Copy

TY - JOUR
TI - Diversity Analysis of MIMO Decode-and-Forward Relay Network by Using Near-ML Decoder
T2 - IEICE TRANSACTIONS on Communications
SP - 2828
EP - 2836
AU - Xianglan JIN
AU - Dong-Sup JIN
AU - Jong-Seon NO
AU - Dong-Joon SHIN
PY - 2011
DO - 10.1587/transcom.E94.B.2828
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E94-B
IS - 10
JA - IEICE TRANSACTIONS on Communications
Y1 - October 2011
AB - The probability of making mistakes on the decoded signals at the relay has been used for the maximum-likelihood (ML) decision at the receiver in the decode-and-forward (DF) relay network. It is well known that deriving the probability is relatively easy for the uncoded single-antenna transmission with M-pulse amplitude modulation (PAM). However, in the multiplexing multiple-input multiple-output (MIMO) transmission, the multi-dimensional decision region is getting too complicated to derive the probability. In this paper, a high-performance near-ML decoder is devised by applying a well-known pairwise error probability (PEP) of two paired-signals at the relay in the MIMO DF relay network. It also proves that the near-ML decoder can achieve the maximum diversity of M_SM_D+M_R min (M_S,M_D), where M_S, M_R, and M_D are the number of antennas at the source, relay, and destination, respectively. The simulation results show that 1) the near-ML decoder achieves the diversity we derived and 2) the bit error probability of the near-ML decoder is almost the same as that of the ML decoder.
ER -