ℓ1/2-Regularization Based Sparse Channel Estimation for MmWave Massive MIMO Systems

Abstract

In the millimeter-wave (mmWave) massive MIMO system, the accuracy of channel estimation directly affects the performance of precoding at the transmitter and detection at the receiver. Hence, it is very important to obtain accurate channel state information (CSI). Considering the channel sparsity of mmWave massive MIMO with hybrid precoding, this paper proposes a ℓ_{1/2}-regularization based sparse channel estimation scheme. The basic idea of the proposed method is to formulate the sparse channel estimation to a compressed sensing problem. Specifically, the scheme firstly constructs an objective function, which is a weighted sum of the ℓ_{1/2}-regularization and the data fitting error. Then optimizes it by means of the gradient descent method iteratively and the weight parameter in the function is also updated each time. Different from the conventional schemes, our proposed scheme can avoid the quantization error and finally achieve super-resolution performance. Simulation results verify that the proposed algorithm can achieve better performance than some recently proposed algorithms.