TY - JOUR
T1 - Low-Complexity ML Detectors for Generalized Spatial Modulation Systems
AU - Lin, Chun Tao
AU - Wu, Wen-Rong
AU - Liu, Chia Yu
N1 - Publisher Copyright:
© 1972-2012 IEEE.
PY - 2015/11
Y1 - 2015/11
N2 - Spatial modulation (SM) combined with spatial multiplexing is a newly developed transmission scheme in multiple-input multiple-output (MIMO) systems. The resultant system, referred to as generalized SM (GSM), can use the maximum-likelihood (ML) detector jointly detecting the antenna-subset (AS) index and symbol vector. As known, the ML detector can achieve optimum performance; however, its computational complexity can be prohibitively high when the dimension of the GSM system is large. In this paper, we propose new methods to solve the problem. The main idea is to split the detection into two stages, one for the AS index and the other for the symbol vector. For the detection of the AS index, we develop two methods, referred to as Gaussian approximation and QR projection. Once the AS index is detected, conventional low-complexity ML detectors can be applied for the detection of the symbol vector. The diversity order for the proposed methods are further analyzed and an enhanced method is also proposed to achieve near-optimum performance. Finally, the proposed methods are extended to conduct soft detection of GSM systems. Simulations show that our methods significantly outperform existing ones while the detection complexity remains similar.
AB - Spatial modulation (SM) combined with spatial multiplexing is a newly developed transmission scheme in multiple-input multiple-output (MIMO) systems. The resultant system, referred to as generalized SM (GSM), can use the maximum-likelihood (ML) detector jointly detecting the antenna-subset (AS) index and symbol vector. As known, the ML detector can achieve optimum performance; however, its computational complexity can be prohibitively high when the dimension of the GSM system is large. In this paper, we propose new methods to solve the problem. The main idea is to split the detection into two stages, one for the AS index and the other for the symbol vector. For the detection of the AS index, we develop two methods, referred to as Gaussian approximation and QR projection. Once the AS index is detected, conventional low-complexity ML detectors can be applied for the detection of the symbol vector. The diversity order for the proposed methods are further analyzed and an enhanced method is also proposed to achieve near-optimum performance. Finally, the proposed methods are extended to conduct soft detection of GSM systems. Simulations show that our methods significantly outperform existing ones while the detection complexity remains similar.
KW - Gaussian approximation
KW - QR projection
KW - generalized spatial modulation
KW - log-likelihood ratio
KW - maximum-likelihood detection
UR - http://www.scopus.com/inward/record.url?scp=84959512014&partnerID=8YFLogxK
U2 - 10.1109/TCOMM.2015.2469781
DO - 10.1109/TCOMM.2015.2469781
M3 - Article
AN - SCOPUS:84959512014
SN - 0090-6778
VL - 63
SP - 4214
EP - 4230
JO - IEEE Transactions on Communications
JF - IEEE Transactions on Communications
IS - 11
M1 - 7208805
ER -