Joint multi-user detection and channel estimation method for grant-free NOMA systems under impulsive noise interference

Aiming at the widespread impulsive noise interference background in grant-free non-orthogonal multiple access (NOMA) systems, a joint multi-user detection, channel estimation and impulsive noise estimation method based on temporally multiple sparse Bayes learning and least square was proposed. Firstly, the joint sparsity of sporadic user communications and burst impulsive noise under multi-antenna reception was used to model the multi-user detection, channel estimation and impulsive noise estimation problems into a compressed sensing problem of multiple measurement vector. Then, the temporally multiple sparse Bayes learning algorithm was used to obtain the support set of active users and burst impulsive noise. Finally, the least square method was used to estimate the channel and impulsive noise. The simulation results show that compared with the existing methods, the proposed method has significant advantages in the performance of multi-user detection, channel estimation and impulsive noise estimation. This conclusion is further validated by the symbol error rate.