An efficient binary salp swarm algorithm for user selection in multiuser MIMO antenna systems

The past ten years have seen notable research activity and significant advancements in multiuser multiple-input multiple-output (MU-MIMO) antennas. An MU-MIMO antenna system must accommodate many subscribers without additional bandwidth or energy. User scheduling becomes a critical strategy to take...

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Vydané v:Scientific reports Ročník 15; číslo 1; s. 16421 - 17
Hlavní autori: Sasikumar, A., Ravi, Logesh, Devarajan, Malathi, Almazyad, Abdulaziz S., De, Shuvodeep, Xiong, Guojiang, Mousavirad, Seyed Jalaleddin, Mohamed, Ali Wagdy
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 12.05.2025
Nature Publishing Group
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639/166/987
639/705/1042
Algorithms
Antenna
Antennas
Binary salp swarm algorithm
Heterogeneity
Humanities and Social Sciences
Metaheuristics optimization
multidisciplinary
Multiuser MIMO
Pollination
Problem solving
Scheduling
Science
Science (multidisciplinary)
User scheduling
User scheduling
Multiuser MIMO
Binary salp swarm algorithm
Metaheuristics optimization
Antenna
ISSN:2045-2322, 2045-2322
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Shrnutí:The past ten years have seen notable research activity and significant advancements in multiuser multiple-input multiple-output (MU-MIMO) antennas. An MU-MIMO antenna system must accommodate many subscribers without additional bandwidth or energy. User scheduling becomes a critical strategy to take advantage of multiuser heterogeneity and acquire maximum gain in systems where the total number of recipients exceeds the number of transmitting antennas. Due to their high computational cost, many user selection methods currently in use, such as greedy algorithms and exhaustive search are unsuitable for MU-MIMO systems. A suitable scheduling mechanism is essential for the various users in an MU-MIMO system to utilise bandwidth and enhance the system’s total rate effectively. In this article, we proposed a user and antenna scheduling with a population-based meta-heuristic approach, namely the binary salp swarm algorithm (binary SSA), to increase the system sum rate with low computing complexity. We specifically used a population-based meta-heuristics optimisation technique to simulate the user scheduling problem in MU-MIMO systems, characterising complicated issues with binary decisions. Additionally, binary SSA significantly outperforms existing population-based models, such as the binary bat algorithm (binary BA), PSO, SSA, FPA and binary flower pollination algorithm (binary FPA), regarding system throughput/sum rate. The proposed binary SSA technique also effectively achieves a system sum rate compared to a random search scheme and other existing suboptimal scheduling methods. Compared to binary BA and binary FPA approaches, the binary SSA has a higher convergence rate and superior searching capabilities. The simulation outcomes show the proposed binary SSA-based scheduling scheme delivers noticeable performance benefits.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-00772-2