Discrete Spatial Compression Beyond Beamspace Channel Sparsity Based on Branch-and-Bound



SOURCE  IEEE ICC 2018, May 20-24, Kansas City, 2018

Published Date:2018-05


One of the most challenging issues in deploying massive multiple-input multiple-output (MIMO) systems is the significant radio-frequency (RF) front-end complexity, hardware cost and power consumption. Towards this end, the beamspace- MIMO based approach is a promising solution. In this paper, we first show that traditional beamspace-MIMO approaches suffer 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 beamspace transformation 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 are obtained based on the branch-andbound (BB) approach. The key to the BB-based solution is to solve the embodied sub-problem, whose solution is derived in a closed-form. Link-level simulation results based on realistic channel models and LTE parameters are presented which show that the proposed schemes can reduce the number of RF chains by up to 25% with a one-bit phase-shifter-network.

This entry was posted in Publications and tagged , . Bookmark the permalink.

Comments are closed.