Development of Adaptive Resource Allocation and Interference Mitigation for Spectrum Sharing in D2D-Enabled 5G Heterogeneous Networks: A Case Study of Urban Microcell Environments
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Abstract
Device-to-device (D2D) communication in heterogeneous networks (HetNets) poses significant challenges in resource allocation and interference management, especially within 5G networks where spectrum sharing between cellular users (CUEs) and D2D user equipment (DUEs) is critical. This study developed an adaptive resource allocation framework using Long Short-Term Reinforcement Learning (LSRL), which integrated Long Short-Term Memory (LSTM) networks with Deep Reinforcement Learning (DRL) technique. The proposed approach addressed the dynamic nature of interference in urban microcell environments by leveraging a Hierarchical Data Format (HDF5) dataset generated from network simulations. These simulations incorporate diverse scenarios, including varying user densities, transmission power levels, and interference conditions. The LSRL-based scheme was evaluated against conventional DRL methods, demonstrating notable improvements in network performance. Specifically, the proposed framework achieved up to a 6.67% increase in sum throughput and an 8.2% enhancement in power efficiency, even under dense user conditions. Additionally, the LSRL model proved resilient to variations in D2D pair distances, maintaining robust spectral efficiency and quality of service (QoS). These findings underscore the potential of the LSRL-based adaptive approach for improving resource management in 5G HetNets, particularly in dense urban deployments, and provide valuable insights for optimizing next-generation wireless communication systems.
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