For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
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
IEICE TRANSACTIONS on Communications
Publication Date: 2011/10/01
Online ISSN: 1745-1345
Print ISSN: 0916-8516
Type of Manuscript: PAPER
Category: Wireless Communication Technologies
decode-and-forward (DF), diversity, maximum-likelihood (ML), multiple-input multiple-output (MIMO), pairwise error probability (PEP), relay,
Full Text: PDF(1.2MB)
>>Buy this Article
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 MSMD+MR min (MS,MD), where MS, MR, and MD 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.