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论文类型： 期刊论文

发表时间： 2017-06-01

发表刊物： IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY

收录刊物： Scopus、SCIE、EI

卷号： 66

期号： 6

页面范围： 5473-5477

ISSN号： 0018-9545

关键字： Computational complexity; detection algorithm; generalized space shift keying (GSSK); maximum likelihood detection; sparse reconstruction (SR)

摘要： In this paper, we propose a novel generalized space shift keying (GSSK) detection algorithm by exploiting the inherent sparse property of GSSK signal. In particular, we formulate the GSSK detection into a sparse convex optimization problem. The key contribution of the proposed algorithm lies in the strategical adoption and transformation of the sparse reconstruction (SR) from image processing to GSSK detection. The proposed SR detector achieves better performance than all existing suboptimal solutions with comparable computational complexity. In order to further improve the performance of the SR algorithm, we develop another approach, i.e., the iterative SR algorithm, which utilizes the result of the SR algorithm as the start point and enhances the detection performance gradually in each iteration. The simulation results confirm the efficiency of both of the proposed algorithms.