Optimization of Device-to-Device Communication in Future Cellular Networks using Machine Learning
DOI:
https://doi.org/10.54228/mjaret0624014Keywords:
Device-to-Device Communication; Machine Learning; Deep Reinforcement Learning; 5G Networks; Spectrum Efficiency; Power Optimization; User Association; Internet of Things; Cellular NetworksAbstract
It provides a novel approach to optimise device-to-device (D2D) communication in next generation cellular networks through advanced machine learning techniques. A novel deep reinforcement learning (DRL) framework for dynamic resource allocation and user association in device-to-device (D2D)-enabled networks is proposed in the paper. By leveraging real-time feedback on the network state and user mobility information, the spectrum resource utilisation can be optimised at a level of granularity not achievable through conventional optimisation methods, to maximise the spectrum efficiency and minimise interference. The experiments were carried out on a simulated 5G network with 1000 D2D pairs, which demonstrate a 30% higher spectral efficiency and 25% lower interference than the traditional optimisation methods. The proposed model was able to maintain a robust performance across different network densities and mobility scenarios, which demonstrates promising potentials for future D2D communication in the next generation cellular networks. This research conducts an important step in the development of more intelligent and adaptive wireless communication systems, facilitating the future rollout of ultra-dense IoT environments.
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