NDLI: Signal Detection in MIMO-OFDM Systems Based on SSDE Algorithm

Content Provider	Springer Nature Link
Author	Hu, Fengye Du, Yu Cen, Ling Ma, Kai Wang, Lu
Copyright Year	2015
Abstract	Device-to-device communication enables to improve the application performance of multi-input multi-output (MIMO) technology. In a multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system, its performance is largely reflected by the signal detection algorithm used in receiver. As a sub-optimal maximum likelihood (ML) detection method, the Selective Spanning with Fast Enumeration algorithm can be successfully applied in MIMO-OFDM systems with high-order modulation. However, its Fast Enumeration scheme calculates constellation points based on fixed formula, which tends to yield pseudo constellation points outside of constellation maps, and consequently cannot work well in low-order modulation. To address this, a selective spanning with direct enumeration (SSDE) algorithm is proposed in this paper. Simulation results proved that the SSDE can achieve similar detection performance at a much lower computational cost in comparison with the ML method. The performance in terms of bit error rate (BER) obtained by SSDE method is also superior to those from the Minimum mean square error and Zero forcing detection algorithms with a huge savings in computational load. By adjusting the parameters used in the SSDE, the tradeoff between BER and computation complexity can be flexibly changed to satisfy specific design requirements in different applications.
Starting Page	2709
Ending Page	2725
Page Count	17
File Format	PDF
ISSN	09296212
Journal	Wireless Personal Communications
Volume Number	82
Issue Number	4
e-ISSN	1572834X
Language	English
Publisher	Springer US
Publisher Date	2015-02-11
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	MIMO-OFDM SSDE Sub-optimal ML detection ML detection Communications Engineering, Networks Signal, Image and Speech Processing Computer Communication Networks
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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