IEEE Transactions on Communications, submitted on June 25, 2019.

Base Station Antenna Selection for Low-Resolution ADC Systems

Jinseok Choi (1), Junmo Sung (1), Narayan Prasad (2), Xiao-Feng Qi (2), Brian L. Evans (1), and Alan Gatherer (3)

(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 - junmo.sung@utexas.edu - bevans@ece.utexas.edu

(2) Futurewei Technologies, Bridgewater, New Jersey USA.
narayan.prasad@futurewei.com - xiao.feng.qi@futurewei.com

(3) Futurewei Technologies, Plano, Texas USA.
alan.gatherer@futurewei.com

Paper Draft (on arXiv)

Multiantenna Communications Project

Abstract

This paper investigates antenna selection at a base station with large antenna arrays and low-resolution analog-to-digital converters. For downlink transmit antenna selection for narrowband channels, we show
  1. a selection criterion that maximizes sum rate with zero-forcing precoding equivalent to that of a perfect quantization system;
  2. maximum sum rate increases with number of selected antennas;
  3. derivation of the sum rate loss function from using a subset of antennas; and
  4. unlike high-resolution converter systems, sum rate loss reaches a maximum at point of total transmit power and decreases beyond that point to converge to zero.
For wideband OFDM systems, our results hold when entire subcarriers share a common subset of antennas. For uplink receive antenna selection for narrowband channels, we
  1. generalize a greedy antenna selection criterion to capture tradeoffs between channel gain and quantization error;
  2. propose a quantization-aware fast antenna selection algorithm using the criterion; and
  3. derive a lower bound on sum rate achieved by the proposed algorithm based on submodular functions.
For wideband OFDM systems, we extend our algorithm and derive a lower bound on its sum rate. Simulation results validate theoretical analyses and show increases in sum rate over conventional algorithms.


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Last Updated 07/01/19.