An In-depth Investigation of AI-driven Dynamic Spectrum Allocation in Cellular Networks

S. M. Ekolama; D. Ebregbe

S. M. Ekolama
D. Ebregbe

Keywords: Mobile, GSM, Call, Cellular Network, Gateway, Communication, Artificial Neural Network, GSM Operation, predictive maintenance

Abstract

The study explored the use of artificial intelligence (AI) model for dynamic spectrum allocation in cellular networks. It aims to optimise spectrum allocation and address static issues by adaptively allocating frequency bands based on real-time demand. The research examines conventional methods of allocating spectrum and presents AI as a potential solution to spectrum shortage problems. Deep-Q algorithm was chosen to develop the model, which is built as a framework of components, system architecture, and methods that could be integrated into existing networks to facilitate dynamic spectrum allocation. This approach enhances real-time spectrum allocation and dynamic decision-making to adaptively assignment frequency. The model was evaluated for its throughput, spectrum efficiency of available spectrum management and other metrics in both simulated scenarios by representing various network topologies and traffic situations, as well as real-life network. It was observed that the model exhibits 88 percent efficiency in intelligently managing available spectrum. This shows that AI-driven dynamic spectrum allocation is far advantageous compared to conventional static allocation approach with limitations, and other factors. The achievement of 88 percent spectrum allocation through our AI-driven dynamic spectrum allocation, shows that the study contributed to the body of knowledge on cellular network management.

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