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We propose a novel randomized channel sparsifying hybrid precoding (RCSHP)design to reduce the signaling overhead of channel estimation and the hardwarecost and power consumption at the base station (BS), in order to fully harvestbenefits of frequency division duplex (FDD) massive multiple-inputmultiple-output (MIMO) systems. RCSHP allows time-sharing among multiple analogprecoders, each serving a compatible user group. The analog precoder is adaptedto the channel statistics to properly sparsify the channel for the associateduser group, such that the resulting effective channel (product of channel andanalog precoder) not only has enough spatial degrees of freedom (DoF) to servethis group of users, but also can be accurately estimated under the limitedpilot budget. The digital precoder is adapted to the effective channel based onthe duality theory to facilitate the power allocation and exploit the spatialmultiplexing gain. We formulate the joint optimization of the time-sharingfactors and the associated sets of analog precoders and power allocations as ageneral utility optimization problem, which considers the impact of effectivechannel estimation error on the system performance. Then we propose anefficient stochastic successive convex approximation algorithm to provablyobtain Karush-Kuhn-Tucker (KKT) points of this problem.

Randomized Channel Sparsifying Hybrid Precoding for FDD Massive MIMO Systems