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Low Complexity Detection for Quadrature Spatial Modulation Systems
by
Li, Jun
, Yu, Wenjun
, Yan, Yier
, Lee, Moon Ho
, Jiang, Xueqin
, Song, Sangseob
in
Algorithms
/ Antennas
/ Codes
/ Communications Engineering
/ Complexity
/ Computer Communication Networks
/ Efficiency
/ Engineering
/ Modulation
/ Networks
/ Quadratures
/ Random variables
/ Signal,Image and Speech Processing
2017
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Low Complexity Detection for Quadrature Spatial Modulation Systems
by
Li, Jun
, Yu, Wenjun
, Yan, Yier
, Lee, Moon Ho
, Jiang, Xueqin
, Song, Sangseob
in
Algorithms
/ Antennas
/ Codes
/ Communications Engineering
/ Complexity
/ Computer Communication Networks
/ Efficiency
/ Engineering
/ Modulation
/ Networks
/ Quadratures
/ Random variables
/ Signal,Image and Speech Processing
2017
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Do you wish to request the book?
Low Complexity Detection for Quadrature Spatial Modulation Systems
by
Li, Jun
, Yu, Wenjun
, Yan, Yier
, Lee, Moon Ho
, Jiang, Xueqin
, Song, Sangseob
in
Algorithms
/ Antennas
/ Codes
/ Communications Engineering
/ Complexity
/ Computer Communication Networks
/ Efficiency
/ Engineering
/ Modulation
/ Networks
/ Quadratures
/ Random variables
/ Signal,Image and Speech Processing
2017
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Low Complexity Detection for Quadrature Spatial Modulation Systems
Journal Article
Low Complexity Detection for Quadrature Spatial Modulation Systems
2017
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Overview
Quadrature spatial modulation (QSM), where the real and imaginary parts of the transmitted symbol are transmitted separately, was recently proposed to increase the spectral efficiency of spatial modulation. An optimum maximum likelihood (ML) detection was introduced in QSM systems, which leads to high complexity as the exhausting search of all possible transmit antennas. In this paper, a low complexity detection, called signal vector based minimum mean square error (SVMMSE), is proposed for QSM systems. In the proposed SVMMSE detection, we estimate the transmit antenna indices and the transmitted symbol by considering the active transmit antennas in two different scenarios. Simulation results show that the performance of our SVMMSE detection is very close to the ML detection and complexity analysis shows that the computational complexity of the proposed SVMMSE detection is significantly reduced compared with that of the ML detection.
Publisher
Springer US,Springer Nature B.V
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