Beamspace Equalization for mmWave Massive MIMO: Algorithms and VLSI Implementations
Seyed Hadi Mirfarshbafan, Christoph Studer

TL;DR
This paper introduces beamspace equalization algorithms and VLSI architectures for mmWave massive MIMO, significantly reducing power consumption and increasing throughput by leveraging channel sparsity.
Contribution
It proposes new beamspace data detection algorithms and VLSI designs that outperform existing solutions in power efficiency and throughput for mmWave massive MIMO systems.
Findings
Up to 54% power savings with the proposed CSPADE equalizer.
Achieves highest throughput among existing massive MIMO detectors.
Up to 66% power savings with MAC-based CSPADE architecture.
Abstract
Massive multiuser multiple-input multiple-output (MIMO) and millimeter-wave (mmWave) communication are key physical layer technologies in future wireless systems. Their deployment, however, is expected to incur excessive baseband processing hardware cost and power consumption. Beamspace processing leverages the channel sparsity at mmWave frequencies to reduce baseband processing complexity. In this paper, we review existing beamspace data detection algorithms and propose new algorithms as well as corresponding VLSI architectures that reduce data detection power. We present VLSI implementation results for the proposed architectures in a 22nm FDSOI process. Our results demonstrate that a fully-parallelized implementation of the proposed complex sparsity-adaptive equalizer (CSPADE) achieves up to 54% power savings compared to antenna-domain equalization. Furthermore, our fully-parallelized…
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Taxonomy
TopicsMillimeter-Wave Propagation and Modeling · Advanced Wireless Communication Techniques · Advanced MIMO Systems Optimization
