Optimal Discrete Spatial Compression for Beamspace Massive MIMO Signals - 2018

PROJECT TITLE :

Optimal Discrete Spatial Compression for Beamspace Massive MIMO Signals - 2018

ABSTRACT:

Deploying a large variety of antennas at the bottom station aspect can boost the cellular system performance dramatically. This however involves significant additional radio-frequency (RF) front-end complexity, hardware cost, and power consumption. To deal with this issue, the beamspace-multiple-input-multiple-output (beamspace-MIMO)-based approach is considered as a promising answer. In this Project, we tend to 1st show that the ancient beamspace-MIMO suffers from spatial power leakage and imperfect channel statistics estimation. A beam combination module is hence proposed, that consists of a tiny variety (compared with the amount of antenna parts) of low-resolution (presumably one-bit) digital (discrete) phase shifters after the beamspace transformation module to further compress the beamspace signal dimensionality, such that the quantity of RF chains can be reduced beyond beamspace transformation and beam choice. The optimum discrete beam combination weights for the uplink are obtained primarily based on the branch-and-sure (BB) approach. The key to the BB-based mostly solution is to resolve the embodied subproblem, whose resolution is derived in a very closed-type. Thereby, a sequential greedy beam combination theme with linear-complexity (w.r.t. the quantity of beams within the beamspace) is proposed. Link-level simulation results primarily based on realistic channel models and long-term-evolution parameters are presented which show that the proposed schemes can reduce the amount of RF chains by up to twenty fivepercent with a 1-bit digital phase-shifter-network.

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