Sparse angular reciprocity learning for massive MIMO channel estimation

Abstract

Sparse angular reciprocity refers to the fact that downlink and uplink channels share common sparsity in angular domain. This sophisticated sparsity can be exploited to improve the channel estimation performance for massive multiple-input multiple-output (MIMO) systems. All the existing sparse angular reciprocity methods solve the uplink channel estimation problem independently with an LS estimator in the first stage, and then assist the downlink channel estimation with the sparse angular reciprocity in the second stage. Such a two-stage scheme, as well as the prior LS estimator, will inevitably result in performance loss. To overcome the drawback, we consider the downlink and uplink channel estimation problems as a whole one, and present a new sparse angular reciprocity learning framework to leverage the sparse angular reciprocity. Since the framework provides a joint solution for the downlink and uplink channel estimation in the sense of Bayesian optimality, it is expected to yield higher channel estimation accuracy. Moreover, we devise an efficient variational Bayesian inference (VBI)-based algorithm to automatically decouple the uplink channel. The proposed method can further enhance the channel estimation performance as the prior LS estimator is avoided. Another advantage is that it can be applied to the underdetermined uplink channel estimation problem, where the number of uplink pilot symbols is less than the number of MUs. Simulation results verify the superiority of the proposed method.