Proc. IEEE Global Communications Conference, Dec. 4-8, 2017, Singapore, submitted.

ADC Bit Optimization for Spectrum- and Energy-Efficient Millimeter Wave Communications

Jinseok Choi (1), Junmo Sung (1), Brian L. Evans (1) and Alan Gatherer (2)

(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) Huawei Technologies, Plano, Texas USA

Paper Draft - Archive Page - Slides

Related paper: "ADC Bit Allocation for mmWave Massive MIMO Communication Receivers" (2017)

Related poster: "ADC Bit Allocation under a Power Constraint for MmWave Massive MIMO Communication Receivers"

Multiantenna Communications Project

Abstract

A spectrum- and energy-efficient system is essential for millimeter wave communication systems that require large antenna arrays with power-demanding ADCs. We propose an ADC bit allocation (BA) algorithm that solves a minimum mean square quantization error problem under a power constraint. Unlike existing BA methods that only consider an ADC power constraint, the proposed algorithm regards total receiver power constraint for a hybrid analog-digital beamforming architecture. The major challenge is the non-linearities in the minimization problem. To address this issue, we first convert the problem into a convex optimization problem through real number relaxation and substitution of ADC resolution switching power with constant average switching power. Then, we derive a closed-form solution by fixing the number of activated radio frequency (RF) chains M. Leveraging the solution, the binary search finds the optimal M and its corresponding optimal solution. We also provide an off-line training and modeling approach to estimate the average switching power. Simulation results validate the spectral and energy efficiency of the proposed algorithm. In particular, existing state-of-the-art digital beamformers can be used in the system in conjunction with the BA algorithm as it makes the quantization error negligible in the low-resolution regime.


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Last Updated 04/15/17.