Performance Evaluation and Optimisation of Spectrum Management in Communication Systems by GA

Ahmed Thabit; Bassam  Abd; Ali  Al-Askery; Haider  Oleiwi

doi:10.22153/kej.2025.10.002

Authors

Ahmed A. Thabit Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq
Bassam H. Abd Electrical Engineering College, University of Technology, Baghdad, Iraq
Ali J. Al-Askery Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq
Haider W. Oleiwi Department of Electronic and Electrical Engineering, Brunel University London, UK

DOI:

https://doi.org/10.22153/kej.2025.10.002

Keywords:

Cognitive Radio; Crossover; Detection Rate; Dynamic Spectrum Access; Genetic Algorithm; Optimisation; Spectrum Sensing

Abstract

Whilst wireless communication technologies proliferate, putting extra demand on the finite radio frequency spectrum and leading to issues of congestion, underutilisation and interference, this dissertation presents a modern spectrum management model on the binary genetic algorithm (BGA) capable of improving detection accuracy and adaptive spectrum access in cognitive radio networks (CRNs). BGA follows binary encoding to determine optimum weighting factors for secondary users in a CRN scenario with a much faster performance and better reliability than conventional genetic approaches. In the cooperative spectrum-sensing scheme proposed in this paper, multiple secondary users will forward their local sensing outcomes to a fusion centre in which BGA optimisation will fine-tune the weighting coefficients throughout the soft decision fusion mechanism. The algorithm then evolves from one generation to the next through the application of selection, crossover and mutation operations to discover the best configuration. Extensive simulation experiments were conducted to study the effects of the critical genetic parameters of mutation probability, crossover rate and population size on detection capability. The results indicate that the optimised BGA framework can achieve detection probability close to 96%, false alarm rate of 0.1, mutation rate of 0.12 and bit error rate of around 7 × 10⁻⁵ even when the signal-to-noise ratio is extremely low at –15 dB. In addition, the comparative evaluation showed the definite superiority of the proposed algorithm when tested against conventional algorithms, such as energy detection, matched filtering and neural network-based convolutions, when subjected to challenging and noise-prone conditions. The work further affirms the applicability of evolutionary algorithms in enhancing the cognitive intelligence of CRs and presents a scalable solution for spectrum management in existing 5G systems and future 6G frameworks.

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