Optimal Discrete Spatial Compression for Beamspace Massive MIMO Signals

姜之源TSP

LANGUAGE English

SOURCE IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 66, NO. 9, MAY 1, 2018

Published Date:2018-05

ABSTRACT

Abstract—Deploying a massive number of antennas at the base station side can boost the cellular system performance dramatically. This however involves significant additional radio-frequency (RF) front-end complexity, hardware cost, and power consumption. To address this issue, the beamspace-multiple-input-multipleoutput (beamspace-MIMO)-based approach is considered as a promising solution. In this paper, we first show that the traditional beamspace-MIMO suffers from spatial power leakage and imperfect channel statistics estimation. A beam combination module is hence proposed, which consists of a small number (compared
with the number of antenna elements) of low-resolution (possibly one-bit) digital (discrete) phase shifters after the beamspace transformation module to further compress the beamspace signal dimensionality, such that the number of RF chains can be reduced beyond beamspace transformation and beam selection. The optimum
discrete beam combination weights for the uplink are obtained based on the branch-and-bound (BB) approach. The key to the BB-based solution is to solve the embodied subproblem, whose solution is derived in a closed-form. Thereby, a sequential greedy beam combination scheme with linear-complexity (w.r.t. the number
of beams in the beamspace) is proposed. Link-level simulation results based on realistic channel models and long-term-evolution parameters are presented which show that the proposed schemes can reduce the number of RF chains by up to 25% with a one-bit digital phase-shifter-network.