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

Joao Henrique Inacio de Souza, Abolfazl Amiri, Taufik Abrão, Elisabeth De Carvalho, Petar Popovski

Research output: Contribution to journalJournal articleResearchpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Article number9435098
JournalI E E E Transactions on Vehicular Technology
Volume70
Issue number7
Pages (from-to)6713-6725
Number of pages13
ISSN0018-9545
DOIs
Publication statusPublished - 2021

Keywords

  • Antennas
  • Computer architecture
  • Extra-large scale massive MIMO (XL-MIMO)
  • Massive MIMO
  • Radio frequency
  • Resource management
  • Signal processing algorithms
  • Transceivers
  • antenna selection (AS)
  • distributed signal processing
  • genetic algorithm (GA)
  • resource allocation (RA)

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