IEEE Transactions on Communications, vol. 69, no. 2, pp. 946--961, Feb. 2021, DOI 10.1109/TCOMM.2020.3036689.

Quantized Massive MIMO Systems with Multicell Coordinated Beamforming and Power Control

Jinseok Choi (1), Yunseong Cho (2) and Brian L. Evans (2)

(1) Department of Electrical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919 Republic of Korea jinseokchoi@unist.ac.kr

(2) Department of Electrical and Computer Engineering, Wireless Networking and Communications Group, The University of Texas at Austin, Austin, Texas USA
yscho@utexas.edu - bevans@ece.utexas.edu

Paper on IEEE Explore - arXiv

Multiantenna Communications Project

Abstract

In this paper, we investigate a coordinated multipoint (CoMP) beamforming and power control problem for base stations (BSs) with a massive number of antenna arrays under coarse quantization at low-resolution analog-to-digital converters (ADCs) and digital-to-analog converter (DACs). Unlike high-resolution ADC and DAC systems, non-negligible quantization noise that needs to be considered in CoMP design makes the problem more challenging. We first formulate total power minimization problems of both uplink (UL) and downlink (DL) systems subject to signal-to-interference-and-noise ratio (SINR) constraints. We then derive strong duality for the UL and DL problems under coarse quantization systems. Leveraging the duality, we propose a framework that is directed toward a twofold aim: to discover the optimal transmit powers in UL by developing iterative algorithm in a distributed manner and to obtain the optimal precoder in DL as a scaled instance of UL combiner. Under homogeneous transmit power and SINR constraints per cell, we further derive a deterministic solution for the UL CoMP problem by analyzing the lower bound of the SINR. Lastly, we extend the derived result to wideband orthogonal frequency-division multiplexing systems to optimize transmit power and beamformer for all subcarriers. Simulation results validate the theoretical results and proposed algorithms


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Last Updated 02/17/21.