Scaling mmWave MU-MIMO: Information-Theoretic Guidance Using Real-World Data

Advances in millimeter wave (mmWave) radio frequency integrated circuits (RFICs) potentially enable the realization of one RF chain per antenna for arrays with hundreds of antenna elements, opening up the possibility of fully digital beamforming for truly massive multiuser (MU-)MIMO. In order to scale adaptive multiuser detection to such regimes, an attractive option, explored in several recent studies, is to reduce complexity via “beamspace” techniques which exploit the sparsity of mmWave channels. In this paper, we derive information-theoretic benchmarks using measured mmWave channels, comparing the capacity with ideal channel knowledge without complexity constraints, and several related benchmarks, with that attainable with low-complexity linear adaptive multiuser detection strategies in beamspace. We hope that the resulting insights regarding the price paid for various levels of simplification in processing will guide performance/complexity tradeoffs in the design of scalable signal processing algorithms.