Bayesian Inference Algorithms for Multiuser Detection in M2M Communications

Abstract

Machine-to-Machine (M2M) communications will be playing an important role in the development of 5th generation (5G) and future wireless communication systems. Due to the sporadic nature of massive access, Low-Activity Code Division Multiple Access (LA-CDMA) is one of possible multiple access schemes for M2M communications. In the literature, maximum a posterior (MAP) detector has been proposed to detect the active users when the user activity factor is known and small. However, the user activity factor is usually unknown and could be large in practice, which makes the multiuser detection (MUD) a challenging task for LA-CDMA. In this paper, we first introduce sparse Bayesian learning (SBL) method to recover the transmitted signals for LA- CDMA uplink access. The proposed method exploits the sparsity of the transmitted signals and does not require the knowledge of user activity. Furthermore, we add on the known finite-alphabet constraints and introduce Gaussian mixture model (GMM) method to obtain the transmitted signals. Simulation results have shown that the proposed methods outperform the conventional algorithms.