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

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.