Fading-ratio-based selection for massive MIMO systems under line-of-sight propagation

Rafael S. Chaves*, Ediz Cetin, Markus V. S. Lima, Wallace A. Martins

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Massive multiple-input multiple-output (MIMO) enables increased throughput by using spatial multiplexing. However, the throughput may severely degrade when the number of users served by a single base station increases, especially under line-of-sight (LoS) propagation. Selecting users is a possible solution to deal with this problem. In the literature, the user selection algorithms can be divided into two classes: small-scale fading aware (SSFA) and large-scale fading aware (LSFA) algorithms. The LSFA algorithms are good solutions for massive MIMO systems under non LoS propagation since the small-scale fading does not affect the system performance under this type of propagation. For the LoS case, the small-scale fading has a great impact on the system performance, requiring the use of SSFA algorithms. However, disregarding the large-scale fading is equivalent to assuming that all users are equidistant from the base station and experience the same level of shadowing, which is not a reasonable approximation in practical applications. To address this shortcoming, a new user selection algorithm called the fading-ratio-based selection (FRBS) is proposed. FRBS considers both fading information to drop those users that induce the highest interference to the remaining ones. Simulation results considering LoS channels show that using FRBS yields near optimum downlink throughput, which is similar to that of the state-of-the-art algorithm, but with much lower computational complexity. Moreover, the use of FRBS with zero-forcing precoder resulted in 26.28% improvement in the maximum throughput when compared with SSFA algorithms, and 35.39% improvement when compared with LSFA algorithms.
Original languageEnglish
Number of pages11
JournalWireless Networks
Early online date23 Jul 2022
DOIs
Publication statusE-pub ahead of print - 23 Jul 2022

Keywords

  • Greedy algorithm
  • Large-scale fading
  • Line-of-sight channel
  • Massive MIMO
  • User selection

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