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

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 04/05/19.