Joint Sparse Channel Estimation in Downlink NOMA System

Abstract

Non-orthogonal multiple access (NOMA) is regarded as one of the most important techniques for future 5G systems. In the downlink general NOMA schemes, the received NOMA signal will be analyzed via two parallel channel state information (CSI) after sparse multiple path channel fading. In this paper, by exploiting the inherent sparsity of the channel, we proposed a low-complexity joint channel estimation in a single-input and multiple-output antennas system, based on the compressed sensing to detect each layer channel state information. As a comparison, the performance of compressed sensing is better than the conventional method Least-Square (LS) and Minimum Mean Square Error (MMSE).