Quasi-Distributed Antenna Selection for Spectral Efficiency Maximization in Subarray Switching XL-MIMO Systems

TL;DR

This paper introduces a quasi-distributed genetic algorithm approach for antenna selection and power allocation in XL-MIMO systems with subarray switching, improving spectral efficiency while reducing complexity and data exchange.

Contribution

It proposes a novel quasi-distributed resource allocation method for XL-MIMO systems that balances performance and complexity, outperforming centralized algorithms.

Findings

Quasi-distributed GA achieves a good trade-off between performance and complexity.

It outperforms centralized algorithms under certain system settings.

Reduces coordination data compared to centralized processing.

Abstract

In this paper, we consider the downlink (DL) of a zero-forcing (ZF) precoded extra-large scale massive MIMO (XL-MIMO) system. The base-station (BS) operates with limited number of radio-frequency (RF) transceivers due to high cost, power consumption and interconnection bandwidth associated to the fully digital implementation. The BS, which is implemented with a subarray switching architecture, selects groups of active antennas inside each subarray to transmit the DL signal. This work proposes efficient resource allocation (RA) procedures to perform joint antenna selection (AS) and power allocation (PA) to maximize the DL spectral efficiency (SE) of an XL-MIMO system operating under different loading settings. Two metaheuristic RA procedures based on the genetic algorithm (GA) are assessed and compared in terms of performance, coordination data size and computational complexity. One algorithm is based on a quasi-distributed methodology while the other is based on the conventional centralized processing. Numerical results demonstrate that the quasi-distributed GA-based procedure results in a suitable trade-off between performance, complexity and exchanged coordination data. At the same time, it outperforms the centralized procedures with appropriate system operation settings.