Antenna Selection for Large-Scale MIMO Systems with Low-Resolution ADCs

TL;DR

This paper proposes a novel antenna selection algorithm for large-scale MIMO systems with low-resolution ADCs, optimizing capacity by balancing channel gain and quantization error, and demonstrating superior performance over conventional methods.

Contribution

It introduces a generalized objective function for antenna selection that accounts for quantization effects in low-resolution ADCs, enhancing capacity.

Findings

Proposed algorithm outperforms conventional methods in achievable capacity.

The generalized objective function effectively balances channel gain and quantization error.

Simulation results confirm improved performance with the new antenna selection approach.

Abstract

One way to reduce the power consumption in large-scale multiple-input multiple-output (MIMO) systems is to employ low-resolution analog-to-digital converters (ADCs). In this paper, we investigate antenna selection for large-scale MIMO receivers with low-resolution ADCs, thereby providing more flexibility in resolution and number of ADCs. To incorporate quantization effects, we generalize an existing objective function for a greedy capacity-maximization antenna selection approach. The derived objective function offers an opportunity to select an antenna with the best tradeoff between the additional channel gain and increase in quantization error. Using the generalized objective function, we propose an antenna selection algorithm based on a conventional antenna selection algorithm without an increase in overall complexity. Simulation results show that the proposed algorithm outperforms the conventional algorithm in achievable capacity for the same number of antennas.