ASEAN Journal on Science and Technology for Development
Abstract
The increased traffic and heterogeneity in the 5G/B5G network require efficient radio resource management (RRM). However, the existing methods, such as GA and PSO, have poor convergence speed and require proper initial conditions. Additionally, learning-based methods have high computational complexity. Hence, in this paper, a novel AI-assisted Hybrid Channel Allocation (AI–HCA) framework is proposed by using a support vector regression (SVR)-based predictive initialization method and GA-PSO optimization. Simulation results using the 3GPP UMa channel model show that the proposed method has a 28% reduction in call blocking probability (CBP), a 15-25% enhancement in spectral efficiency (SE), and a 18-25% enhancement in energy efficiency (EE) with a faster convergence speed (35-40 iterations) than the existing methods. Additionally, the proposed method has been validated using the ANOVA test (p < 0.05) to confirm the significant improvements. Hence, the proposed AI-assisted Hybrid Channel Allocation framework has the potential to provide a low-complexity, robust, and scalable solution for intelligent radio resource management in 5G/B5G networks.
Keywords
Radio resource management, hybrid optimization, genetic algorithm, particle swarm optimization, support vector regression, 5G/B5G networks
Publication Date
2026
Received Date
29 Dec 2025
Revised Date
05 Apr 2026
Accepted Date
24 Apr 2026
Recommended Citation
Ohatkar, Sharada Narsingrao
(2026)
"AI-Assisted Hybrid GA–PSO Channel Allocation Under 3GPP TR 38.901 UMa for Efficient 5G Radio Resource Management,"
ASEAN Journal on Science and Technology for Development: Vol. 43:
No.
2, Article 5.
DOI: https://doi.org/10.61931/2224-9028.1661
Available at:
https://ajstd.ubd.edu.bn/journal/vol43/iss2/5