Two-Level Sparse Structure-Based Compressive Sensing Detector for Uplink Spatial Modulation With Massive Connectivity

Abstract

Spatial modulation (SM) with high spectrum and energy efficiencies is promising for future massive connectivity communication networks, in which user activities are often sporadic and SM signals are sparse. Such characteristics, if exploited in the detection process, will lead to gains in terms of receiver complexity and performance. This letter proposes a compressive sensing (CS) algorithm for multiuser detection in SM-MIMO systems to efficiently exploit such characteristics. Specifically, the temporal correlation of the user activities and the random transmission nature of SM signals are used to improve detection performance without requiring prior knowledge of the user sparsity level. In addition, the proposed algorithm requires a lower number of BS receive and transmit antennas in each user compared with existing CS-based detection algorithms and has a significantly lower computational complexity.