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In this paper, we study the beamforming design problem in frequency-divisionduplexing (FDD) downlink massive MIMO systems, where instantaneous channelstate information (CSI) is assumed to be unavailable at the base station (BS).We propose to extract the information of the angle-of-departures (AoDs) and thecorresponding large-scale fading coefficients (a.k.a. spatial information) ofthe downlink channel from the uplink channel estimation procedure, based onwhich a novel downlink beamforming design is presented. By separating thesubpaths for different users based on the spatial information and the hiddensparsity of the physical channel, we construct near-orthogonal virtual channelsin the beamforming design. Furthermore, we derive a sum-rate expression and itsapproximations for the proposed system. Based on these closed-form rateexpressions, we develop two low-complexity beam selection schemes and carry outasymptotic analysis to provide valuable insights on the system design.Numerical results demonstrate a significant performance improvement of ourproposed algorithm over the state-of-the-art beamforming approach.

Statistical Beamforming for FDD Downlink Massive MIMO via Spatial Information Extraction and Beam Selection