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In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
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An efficient binary salp swarm algorithm for user selection in multiuser MIMO antenna systems.

A Sasikumar1, Logesh Ravi2,3, Malathi Devarajan4

  • 1Department of Data Science and Business Systems, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India.

Scientific Reports
|May 12, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new scheduling method for multiuser multiple-input multiple-output (MU-MIMO) systems using the binary salp swarm algorithm (binary SSA). It enhances system throughput and reduces computational complexity for better performance.

Keywords:
AntennaBinary salp swarm algorithmMetaheuristics optimizationMultiuser MIMOUser scheduling

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Telecommunications

Background:

  • Multiuser multiple-input multiple-output (MU-MIMO) antenna systems are advancing rapidly.
  • Efficient user scheduling is crucial for MU-MIMO systems to maximize gains without increasing bandwidth or energy.
  • Existing scheduling methods like greedy algorithms and exhaustive search are computationally expensive for MU-MIMO.

Purpose of the Study:

  • To propose an efficient user and antenna scheduling mechanism for MU-MIMO systems.
  • To enhance system sum rate and throughput using a population-based meta-heuristic approach.
  • To address the computational complexity challenges in MU-MIMO user scheduling.

Main Methods:

  • Developed a novel scheduling approach using the binary salp swarm algorithm (binary SSA).
  • Applied population-based meta-heuristics to model MU-MIMO user scheduling as a binary decision problem.
  • Compared the performance of binary SSA against other algorithms like binary BA, PSO, SSA, and binary FPA.

Main Results:

  • The proposed binary SSA significantly outperforms existing population-based models in terms of system sum rate.
  • Binary SSA demonstrates superior performance compared to random search and suboptimal scheduling methods.
  • Binary SSA exhibits a higher convergence rate and enhanced searching capabilities compared to binary BA and binary FPA.

Conclusions:

  • The binary SSA-based scheduling scheme offers a computationally efficient and effective solution for MU-MIMO systems.
  • This approach significantly improves system sum rate and overall performance.
  • The study validates the effectiveness of population-based meta-heuristics for complex scheduling problems in wireless communication.