|| Optimality of the Discrete Fourier Transform for Beamspace Massive MU-MIMO Communication
||Sueda Taner, Cornell University, United States; Christoph Studer, ETH Zurich, Switzerland|
||D7-S1-T2: Beamforming Design
||Tuesday, 20 July, 22:00 - 22:20
||Tuesday, 20 July, 22:20 - 22:40
Beamspace processing is an emerging technique to reduce baseband complexity in massive multiuser (MU) multiple-input multiple-output (MIMO) communication systems operating at millimeter-wave and terahertz frequencies. The high directionality of wave propagation at such high frequencies ensures that only a small number of transmission paths exist between user equipments and basestation (BS). In order to resolve the sparse nature of wave propagation, beamspace processing traditionally computes a spatial discrete Fourier transform (DFT) across a uniform linear antenna array at the BS where each DFT output is associated with a specific beam. In this paper, we study optimality conditions of the DFT for sparsity-based beamspace processing with idealistic mmWave channel models and realistic channels. To this end, we propose two algorithms that learn unitary beamspace transforms using an l4-norm-based sparsity measure, and we investigate their optimality theoretically and via simulations.