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.
Questions & Answers
Q: Does this work only consider the narrowband? Any plans for wideband?
A: Yes, it does.
An extension to wideband can easily be done by using OFDM where each
subcarrier experiences a narroband channel.
Additional tricks would be required for complexity reduction as there
are usually a number of subcarriers in OFDM systems.
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Last Updated 02/25/20.