Multi-User Detection Based on Sparsity Adaptive Matching Pursuit Compressive Sensing for Uplink Grant-free Non-Orthogonal Multiple Access

Abstract

Grant-free Non-Orthogonal Multiple Access (NOMA) combined with Multi-User Detection (MUD) technology can meet the requirements of large connection volume, low signaling overhead and low latency transmission in massive Machine Type Communications (mMTC) scenarios. In the MUD algorithm based on Compressed Sensing (CS), the number of active users is often used as known information, but it is difficult to accurately estimate in the actual communication system. Based on this, this paper proposes a multi-user algorithm (Modified Sparsity Adaptive Matching Pursuit MUD, MSAMP-MUP) to improve the adaptive matching of sparsity. Firstly, the algorithm uses the generalized Dice coefficient matching criterion to select the atom that best matches the residual, and updates the user support set. When the residual energy is close to the noise energy, the iteration is terminated to obtain the final support set; Otherwise, the above criteria are used to update the user support set, and the estimation accuracy of the active users in the support set is improved. In the iteration process, different iteration steps are selected according to the ratio of the last two residual energies, so as to reduce the number of detection iterations. The simulation results show that, compared with the traditional CS-based MUD algorithm, the proposed algorithm reduces the bit error rate by about 9% and the number of iterations by about 10%.