Enhancing D2D Communication for Network Throughput Optimization in 5G and Beyond
DOI:
https://doi.org/10.54228/mjaret0624015Keywords:
D2D communication, 5G networks, machine learning, deep reinforcement learning, network throughput, interference management, resource allocation, user association, power optimization, beyond 5G.Abstract
This research investigates the optimization of Device-to-Device (D2D) communication in 5G and beyond networks for enhancing network throughput using state-of-the-art machine learning techniques. Device-to-Device (D2D) communication is a key feature of 5G and beyond cellular systems, where direct device-to-device communication is allowed. It improves network spectral efficiency and decreases the network latency. However, the resource block allocation and interference management are the key challenges to leverage D2D communication. We propose a DRL-based optimization framework for improving the D2D communication power allocation and user association control to enhance the overall network throughput and decrease the interference. As the network condition changes and user mobility, the proposed framework adapts to control and optimize network throughput and interference. The performance of the proposed approach has been extensively evaluated in a 5G network simulator to show a 32% increase in the overall network throughput and 28% decrease in interference compared to the optimized stochastic optimisation approach.
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