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 -
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.