Aditya Ghosh
Independent Researcher
India
Abstract
In this study, we present a comprehensive investigation into the optimization of channel allocation in GSM networks using artificial neural networks (ANNs). Traditional fixed and dynamic channel allocation schemes face limitations in adapting to fluctuating traffic loads and interference patterns. By leveraging pattern recognition and function approximation capabilities of ANNs, we develop a neural-based allocation engine that predicts optimal channel assignments in real time. The proposed method employs a multilayer perceptron trained on historical traffic and interference data from multiple base transceiver stations (BTS) in a metropolitan GSM deployment circa 2018. Simulation experiments demonstrate that the ANN-based approach reduces call blocking probability by up to 25 % and improves channel utilization efficiency by up to 18 % compared with classical dynamic allocation. These results underscore the potential of neural computation in enhancing spectrum efficiency and quality of service in legacy cellular systems.
Keywords
GSM, channel allocation, artificial neural networks, multilayer perceptron, traffic optimization
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