DocumentCode
68490
Title
Low-Complexity Compressive Sensing Detection for Spatial Modulation in Large-Scale Multiple Access Channels
Author
Garcia-Rodriguez, Adrian ; Masouros, Christos
Author_Institution
Dept. of Electron. & Electr. Eng., Univ. Coll. London, London, UK
Volume
63
Issue
7
fYear
2015
fDate
Jul-15
Firstpage
2565
Lastpage
2579
Abstract
In this paper, we propose a detector, based on the compressive sensing (CS) principles, for multiple-access spatial modulation (SM) channels with a large-scale antenna base station (BS). Particularly, we exploit the use of a large number of antennas at the BSs and the structure and sparsity of the SM transmitted signals to improve the performance of conventional detection algorithms. Based on the above, we design a CS-based detector that allows the reduction of the signal processing load at the BSs particularly pronounced for SM in large-scale multiple-input-multiple-output (MIMO) systems. We further carry out analytical performance and complexity studies of the proposed scheme to evaluate its usefulness. The theoretical and simulation results presented in this paper show that the proposed strategy constitutes a low-complexity alternative to significantly improve the system´s energy efficiency against conventional MIMO detection in the multiple-access channel.
Keywords
MIMO communication; antennas; compressed sensing; wireless channels; CS principles; MIMO detection; MIMO systems; SM channels; SM transmitted signals; antennas; detection algorithms; large-scale multiple access channels; low complexity compressive sensing detection; multiple-access spatial modulation; multiple-input-multiple-output systems; signal processing; spatial modulation; Complexity theory; Detectors; MIMO; Modulation; Signal processing algorithms; Transmitting antennas; Spatial modulation; compressive sensing; energy efficiency; large-scale MIMO; multiple access;
fLanguage
English
Journal_Title
Communications, IEEE Transactions on
Publisher
ieee
ISSN
0090-6778
Type
jour
DOI
10.1109/TCOMM.2015.2434817
Filename
7109850
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