Nonconvex Regularized Gradient Projection Sparse Reconstruction for Massive MIMO Channel Estimation

TL;DR

This paper introduces novel nonconvex regularized gradient projection algorithms for sparse beamspace channel estimation in massive MIMO systems, offering faster convergence and improved accuracy over existing methods.

Contribution

It proposes new algorithms based on nonconvex regularization and difference of convex functions for efficient, accurate massive MIMO channel estimation.

Findings

Significantly improved reconstruction accuracy.

Faster convergence rates compared to existing algorithms.

Lower mean squared error and higher spectral efficiency.

Abstract

Novel sparse reconstruction algorithms are proposed for beamspace channel estimation in massive multiple-input multiple-output systems. The proposed algorithms minimize a least-squares objective having a nonconvex regularizer. This regularizer removes the penalties on a few large-magnitude elements from the conventional l1-norm regularizer, and thus it only forces penalties on the remaining elements that are expected to be zeros. Accurate and fast reconstructions can be achieved by performing gradient projection updates within the framework of difference of convex functions (DC) programming. A double-loop algorithm and a single-loop algorithm are proposed via different DC decompositions, and these two algorithms have distinct computation complexities and convergence rates. Then, an extension algorithm is further proposed by designing the step sizes of the single-loop algorithm. The extension algorithm has a faster convergence rate and can achieve approximately the same level of accuracy as the proposed double-loop algorithm. Numerical results show significant advantages of the proposed algorithms over existing reconstruction algorithms in terms of reconstruction accuracies and runtimes. Compared to the benchmark channel estimation techniques, the proposed algorithms also achieve smaller mean squared error and higher achievable spectral efficiency.