A Unified Precoding Scheme for Generalized Spatial Modulation

被引:25
作者
Cheng, Peng [1 ]
Chen, Zhuo [2 ]
Zhang, J. Andrew [3 ]
Li, Yonghui [1 ]
Vucetic, Branka [1 ]
机构
[1] Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW 2006, Australia
[2] CSIRO DATA61, Marsfield, NSW 2122, Australia
[3] Univ Technol Sydney, Sch Comp & Commun, Ultimo, NSW 2007, Australia
关键词
Spatial modulation; generalized spatial modulation; linear precoding; maximum minimum Euclidean distance; augmented Lagrangian; SEMIDEFINITE RELAXATION; ANTENNA SELECTION; LIMITED FEEDBACK; SYSTEMS; OPTIMIZATION; DIVERSITY; CHANNELS;
D O I
10.1109/TCOMM.2018.2796605
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Generalized spatial modulation (GSM) activates n(t) out of N-t (1 <= n(t) < N-t) available transmit antennas, and information is conveyed through nt modulated symbols as well as the index of the n(t) activated antennas. GSM strikes an attractive tradeoff between spectrum efficiency and energy efficiency. Linear precoding that exploits channel state information at the transmitter enhances the system error performance. For GSM with n(t) = 1 (the traditional SM), the existing precoding methods suffer from high computational complexity. On the other hand, GSM precoding for n(t) >= 2 is not thoroughly investigated in the open literature. In this paper, we develop a unified precoding design for GSM systems, which universally works for all n(t) values. Based on the maximum minimum Euclidean distance criterion, we find that the precoding design can be formulated as a large-scale nonconvex quadratically constrained quadratic program problem. Then, we transform this challenging problem into a sequence of unconstrained subproblems by leveraging augmented Lagrangian and dual ascent techniques. These subproblems can be solved in an iterative manner efficiently. Numerical results show that the proposed method can substantially improve the system error performance relative to the GSM without precoding and features extremely fast convergence rate with a very low computational complexity.
引用
收藏
页码:2502 / 2514
页数:13
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