• An efficient binary salp swarm...

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

Title:
An efficient binary salp swarm algorithm for user selection in multiuser MIMO antenna systems.
Authors:
Sasikumar A; Department of Data Science and Business Systems, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India., Ravi L; Centre for Advanced Data Science, Chennai, Tamil Nadu, 600127, India.; School of Electronics Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, 600127, India., Devarajan M; School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, India., Almazyad AS; Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, 11543, Riyadh, Saudi Arabia., De S; Virginia Tech, Blacksburg, USA., Xiong G; Guizhou Key Laboratory of Intelligent Technology in Power System, College of Electrical Engineering, Guizhou University, Guiyang, 550025, China., Mousavirad SJ; Department of Computer and Electrical Engineering, Mid Sweden University, Sundsvall, Sweden. seyedjalaleddin.mousavirad@miun.se., Mohamed AW; Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, 12613, Egypt.; Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan.; Centre for Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, India.
Source:
Scientific reports [Sci Rep] 2025 May 12; Vol. 15 (1), pp. 16421. Date of Electronic Publication: 2025 May 12.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: PubMed not MEDLINE; MEDLINE
Imprint Name(s):
Original Publication: London : Nature Publishing Group, copyright 2011-
References:
Rev Sci Instrum. 2018 May;89(5):054702. (PMID: 29864883)
Contributed Indexing:
Keywords: Antenna; Binary salp swarm algorithm; Metaheuristics optimization; Multiuser MIMO; User scheduling
Entry Date(s):
Date Created: 20250512 Latest Revision: 20250515
Update Code:
20250515
PubMed Central ID:
PMC12069606
DOI:
10.1038/s41598-025-00772-2
PMID:
40355606
Database:
MEDLINE

Weitere Informationen

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.
(© 2025. The Author(s).)

Declarations. Competing interests: The authors declare no competing interests.