IEEE Transactions on Signal Processing,
vol. 67, no. 9, May 1, 2019, pp. 2410-2425, DOI 10.1109/TSP.2019.2904931.
Two-Stage Analog Combining in Hybrid Beamforming Systems with Low-Resolution ADCs
Jinseok Choi (1),
Gilwon Lee (2), and
Brian L. Evans (1)
(1) Department of Electrical and Computer Engineering,
Wireless Networking and Communications Group,
The University of Texas at Austin,
Austin, TX 78712 USA
jinseokchoi89@gmail.com -
bevans@ece.utexas.edu
(2) Intel Corporation, Santa Clara, CA 95054 USA
gilwon.lee30@gmail.com
Paper draft on
arXiv and
IEEE Explore
Software Release
Multiantenna Communications Project
Abstract
In this paper, we investigate hybrid analog/digital beamforming for
multiple-input multiple-output (MIMO) systems with low-resolution
analog-to-digital converters (ADCs) for millimeter wave (mmWave)
communications.
In the receiver, we propose to split the analog combining subsystem
into a channel gain aggregation stage followed by a spreading stage.
Both stages use phase shifters.
Our goal is to design the two-stage analog combiner to optimize mutual
information (MI) between the transmitted and quantized signals by
effectively managing quantization error.
To this end, we formulate an unconstrained MI maximization problem
without a constant modulus constraint on analog combiners, and derive
a two-stage analog combining solution.
The solution achieves the optimal scaling law with respect to the
number of radio frequency chains and maximizes the MI for homogeneous
singular values of a MIMO channel.
We further develop a two-stage analog combining algorithm to implement
the derived solution for mmWave channels.
By decoupling channel gain aggregation and spreading functions from
the derived solution, the proposed algorithm implements the two
functions by using array response vectors and a discrete Fourier
transform matrix under the constant modulus constraint on each matrix
element.
Therefore, the proposed algorithm provides a near optimal solution for
the unconstrained problem, whereas conventional hybrid approaches offer
a near optimal solution only for a constrained problem.
The closed-form approximation of the ergodic rate is derived for the
algorithm, showing that a practical digital combiner with two-stage
analog combining also achieves the optimal scaling law.
Simulation results validate the algorithm performance and the derived
ergodic rate.
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Last Updated 07/27/19.