Compressive Sensing based Low Complexity User Selection for Massive MIMO Systems

Massive Multiple-input Multiple-output (MIMO) is widely considered as a key enabler of the next-generation networks. In these systems, user selection strategies are important to achieve spatial diversity and maximize spectral efficiency. In this paper, a user selection algorithm is proposed with the reconstruction of the sparse Massive MIMO channel using Compressive Sensing (CS) algorithm. The proposed algorithm eliminates the users based on the channel correlation by employing the CS algorithm which reduces the feedback overhead in the system. The simulation results show that the proposed algorithm outperforms the traditional user selection algorithms in terms of sum data rate and computational complexity. Moreover, the effects of the sparsity level and feedback measurement on the performance are examined.