Efficient Resource Allocation for D2D Communication in Next-Generation Cellular Networks: Ensuring Security and Performance
DOI:
https://doi.org/10.54228/mjaret0624019Keywords:
D2D communication; resource allocation; next-generation cellular systems; 5G networks; social-community-aware; deep reinforcement learning; Quality of Service (QoS); interference managementAbstract
This work proposes a novel framework for efficient resource allocation for Device-to-Device (D2D) communication in next-generation cellular networks. A Deep Reinforcement Learning (DRL) combining a novel social-awareness mechanism and significant amount of social interaction data as well as device locations was proposed to achieve secure and efficient resource management. A two-stage algorithm was proposed to improve resource allocation for ensuring low interference and high network throughput. Evaluation through employing a 5G network simulator for different cell densities and QoS constraints proved the DRL mechanism to perform significantly better on resource allocation by increasing 28% in network throughput and 32% in reducing latency in comparison with state-of-the-art models. The framework secures communication by significantly lowering interference, improving QoS, and optimising D2D communication in next-generation cellular systems.
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