Block-sparse compressive sensing based multi-user and signal detection for generalized spatial modulation in NOMA

The non-orthogonal multiple access (NOMA) technology has been proposed and regarded as one of the potential promising technologies for the future 5G network. The extension of generalized spatial modulation multiple-input multiple-output (MIMO) to NOMA system improves both the spectral and energy efficiencies of the system, while maintaining the massive connectivity and low latency advantages, but it puts forward challenges for the multi-user and signal detection as well. In this paper, we propose a joint user activity and signal detection scheme based on the block-sparse compressive sensing (BS-CS) method in the uplink NOMA system, in which the generalized spatial modulation MIMO technology is used. By exploiting the structure and sparsity of the multi-user generalized spatial modulation signals, we formulate the detection problem into a block-sparse recovery problem. Then a BS-CS based detection algorithm, enhanced structured block-sparse compressive sampling matching pursuit (ESB-CoSaMP), is proposed to detect the active users and transmitted data efficiently. Moreover, the information of active antennas at each user is exploited in ESB-CoSaMP to further improve the accuracy. Simulations show that the proposed detection scheme outperforms the conventional CS and BS-CS based schemes.