Joint Channel Estimation and Feedback with Low Overhead for FDD Massive MIMO Systems

Accurate channel state information (CSI) is essential to realize the potential advantages of massive MIMO. However, the overhead required by conventional channel estimation and feedback schemes will be unaffordable, especially for frequency division duplex (FDD) massive MIMO. To solve this problem, we propose a structured compressive sensing (SCS) based spatio-temporal joint channel estimation and feedback scheme to reduce the required overhead. Particularly, we first propose the non-orthogonal pilots at the base station (BS) under the framework of CS theory. Then, an adaptive structured subspace pursuit (ASSP) algorithm is proposed to jointly estimate channels associated with multiple OFDM symbols at the receiver, whereby the spatio-temporal common sparsity of massive MIMO channels is exploited to improve the channel estimation accuracy. Moreover, we propose a parametric channel feedback scheme, which exploits the sparsity of channels to acquire accurate CSI at the BS with reduced feedback overhead. Simulation results show that the channel estimation performance approaches that of the oracle least squares (LS) channel estimator, and the parametric channel feedback scheme only suffers from a negligible performance loss compared with the complete channel feedback scheme.