Proc. IEEE International Conference on Computing, Networking and Communications, Feb. 17-20, 2020, Big Island, Hawaii, USA.

Versatile Compressive mmWave Hybrid Beamformer Codebook Design Framework

Junmo Sung and Brian L. Evans

Department of Electrical and Computer Engineering, Wireless Networking and Communications Group, The University of Texas at Austin, Austin, TX 78712 USA -

Paper Draft - Presentation Slides - Software Release

Multiantenna Communications Project


Hybrid beamforming (HB) architectures are attractive for wireless communication systems with large antenna arrays because the analog beamforming stage can significantly reduce the number of RF transceivers and hence power consumption. In HB systems, channel estimation (CE) becomes challenging due to indirect access by the baseband processing to the communication channels and due to low SNR before beam alignment. Compressed sensing (CS) based algorithms have been adopted to address these challenges by leveraging the sparse nature of millimeter wave multi-input multi-output (mmWave MIMO) channels. In many CS algorithms for narrowband CE, the hybrid beamformers are randomly configured which does not always yield the low-coherence sensing matrices desirable for those CS algorithms whose recovery guarantees rely on coherence. In this paper, we propose a versatile deterministic HB codebook design framework for CS algorithms with coherence-based recovery guarantees to enhance CE accuracy. Simulation results show that the proposed design can obtain lower channel estimation error and higher spectral efficiency compared with random codebook for phase-shifter-, switch-, and lens-based HB architectures.

Questions & Answers

Q: CS is generally useful in reducing the number of measurements. Is this the case in this work?
A: In this particular work, only the full-training is considered, meaning the number of measurement given to the CS algorithm is the same as the one required by least squares estimator. This is for a fair comparison between the conventional channel estimation algorithm and the CS algorithm with the proposed codebooks.

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Last Updated 02/25/20.