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In this paper, we consider user selection and downlink precoding for anover-loaded single-cell massive multiple-input multiple-output (MIMO) system infrequency division duplexing (FDD) mode, where the base station is equippedwith a dual-polarized uniform planar array (DP-UPA) and serves a large numberof single-antenna users. Due to the absence of uplink-downlink channelreciprocity and the high-dimensionality of channel matrices, it is extremelychallenging to design downlink precoders using closed-loop channel probing andfeedback with limited spectrum resource. To address these issues, a novelmethodology -- active channel sparsification (ACS) -- has been proposedrecently in the literature for uniform linear array (ULA) to design sparsifyingprecoders, which boosts spectral efficiency for multi-user downlinktransmission with substantially reduced channel feedback overhead. Pushingforward this line of research, we aim to facilitate the potential deployment ofACS in practical FDD massive MIMO systems, by extending it from ULA to DP-UPAwith explicit user selection and making the current ACS implementationsimplified. To this end, by leveraging Toeplitz structure of channel covariancematrices, we extend the original ACS using scale-weight bipartite graphrepresentation to the matrix-weight counterpart. Building upon this, we proposea multi-dimensional ACS (MD-ACS) method, which is a generalization of originalACS formulation and is more suitable for DP-UPA antenna configurations. Thenonlinear integer program formulation of MD-ACS can be classified as ageneralized multi-assignment problem (GMAP), for which we propose a simple yetefficient greedy algorithm to solve it. Simulation results demonstrate theperformance improvement of the proposed MD-ACS with greedy algorithm over thestate-of-the-art methods based on the QuaDRiGa channel models.

Downlink Precoding for DP-UPA FDD Massive MIMO via Multi-Dimensional Active Channel Sparsification