Sparse Multi-User Detection With Imperfect Channel Estimation for NOMA Systems Under Non-Gaussian Noise

Abstract

Non-orthogonal multiple access (NOMA) is a promising technology in future wireless communication and multi-user detection (MUD) is the key problem in NOMA system to eliminate inter-user signal interference. Due to the existence of noise and interference, channel estimation is always imperfect which will decrease the performance of MUD. Also, in real systems, the noise does not always follow Gaussian distribution. To deal with these problems, a MUD algorithm with joint channel estimation and signal detection is proposed in this letter. The theory of sparse Bayesian learning and variational message passing is used to solve the sparse estimation problem. Simulation results show the merits of the proposed algorithm.