Dynamic Channel Allocation in Multi-Cell Cellular Systems Using Enhanced Particle Swarm Optimization

International Journal of Computer Science and Technology | Vol. 6, No. 11, November 2004 | pp. 81–88

DOI: 10.46882/2004/IJCST/061102

Research Article

Title: Dynamic Channel Allocation in Multi-Cell Cellular Systems Using Enhanced Particle Swarm Optimization

Names of Authors: Marcus Vance¹, Elena Rostova²

Authors’ Affiliations: ¹Department of Electrical and Electronic Engineering, University of Manchester, Manchester, UK; ²Faculty of Computer Science, Saint Petersburg State University, Saint Petersburg, Russia

Abstract:
Electromagnetic interference and spectrum scarcity pose substantial challenges to modern cellular network infrastructures. This study introduces an Enhanced Particle Swarm Optimization (EPSO) algorithm designed for dynamic channel allocation (DCA) in multi-cell environments. Unlike traditional heuristic approaches that frequently converge into local minima, the EPSO incorporates a chaotic mutation operator that scales adaptively with the system workload. Simulations were evaluated across a standard 49-cell hexagonal grid topology under varying traffic intensities ranging from 10 to 120 Erlangs. The empirical findings show that the proposed EPSO framework drops the call blocking probability (P_b) to less than 0.015 under peak traffic loads, yielding a 14.2% reduction compared to standard genetic algorithms. The average spectrum utilization efficiency improved by μ = 18.7%, while the computational execution time required to reach convergence stabilized at t = 42 ms. This allows for near real-time adaptations to rapid fluctuations in user density.

Keywords: Channel Allocation, Particle Swarm Optimization, Cellular Networks, Call Blocking Probability, Spectrum Efficiency

Manuscript Timeline: Received August 04, 2004; Revised September 15, 2004; Accepted October 11, 2004; Published November 22, 2004

Citation: Vance, M., & Rostova, E. (2004). Dynamic Channel Allocation in Multi-Cell Cellular Systems Using Enhanced Particle Swarm Optimization. International Journal of Computer Science and Technology, 6(11), 81–88.

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