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

Hybrid Beamformer Codebook Design and Ordering for Compressive mmWave Channel Estimation

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
junmo.sung@utexas.edu - bevans@ece.utexas.edu

Paper Draft - Presentation Slides - Software Release

Multiantenna Communications Project

Abstract

In millimeter wave (mmWave) communication systems, beamforming with large antenna arrays is critical to overcome high path losses. Separating all-digital beamforming into analog and digital stages can provide the large reduction in power consumption and small loss in spectral efficiency needed for practical implementations. Developing algorithms with this favorable tradeoff is challenging due to the additional degrees of freedom in the analog stage and its accompanying hardware constraints. In hybrid beamforming systems, for example, channel estimation algorithms do not directly observe the channels, face a high channel count, and operate at low SNR before transmit-receive beam alignment. Since mmWave channels are sparse in time and beam domains, many compressed sensing (CS) channel estimation algorithms have been developed that randomly configure the analog beamformers, digital beamformers, and/or pilot symbols. In this paper, we propose to design deterministic beamformers and pilot symbols for open-loop channel estimation. We use CS approaches that rely on low coherence for their recovery guarantees, and hence seek to minimize the mutual coherence of the compressed sensing matrix. We also propose a precoder column ordering to design the pilot symbols. Simulation results show that our beamformer designs reduce channel estimation error over competing methods.


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Last Updated 10/02/19.