Variational Inference Based Sparse Signal Detection for Next Generation Multiple Access

The next generation multiple access (NGMA) schemes are considered to support massive access for a large number of devices, which motivates us to develop a low-complexity approach for next generation systems. Since the generalized spatial modulation (SM) can be adopted to the system, a number of compressive sensing (CS) reconstruction algorithms are deployed for the detection of sparse signals, while the complexity of CS-based approaches is proportional to the number of antennas. In order to decrease the complexity, we propose a two-stage approach to detect sparse signals, where the received signals are divided into groups. Then, the activity variables of aggregated signals are decided and the sparse signal detection is carried out at the signals belonging to active groups. During the activity variable detection, the variational inference algorithm is applied to determine the activity variables. Moreover, in order to analyze the performance of activity variable detection, the J -divergence is proposed to measure the distance between the distributions, while the approximate expression of J -divergence is derived. Simulation results show that the proposed approach is able to provide good detection performance with low complexity. In addition, the J -divergence is confirmed to be useful as an evaluation metric to measure the detection performance.