International Journal of Computer Networks and Applications (IJCNA)

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ISSN : 2395-0455

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International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

Cooperative Device-to-Device Communication Using Joint Relay Assignment and Channel Allocation Using Deep Learning

Author NameAuthor Details

Md. Tabrej Khan, Ashish Adholiya

Md. Tabrej Khan[1]

Ashish Adholiya[2]

[1]Department Faculty of Computer Science, Pacific Academy of Higher Education and Research University, Udaipur (Rajasthan), India.

[2]Department Faculty of Computer Science, Pacific Academy of Higher Education and Research University, Udaipur (Rajasthan), India.

Cite this Article

DOI: 10.22247/ijcna/2025/06

Pages : 77-92

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Abstract

Fusion centers support sensing and signal processing in decentralized mobile user communications. The backend network is made up of device-to-device (D2D) connections, which use appropriate channel selection to guarantee smooth connectivity. Delays and decreased dependability result from an imbalance in the distribution of channels. In cognitive radio networks, the behavior of primary users affects stability, whereas relay communication maximizes resources. In this regard, a joint channel selection and routing protocol, is proposed in this research based on deep reinforcement learning. The goal of this research is to minimize interference and optimize network performance by developing a deep reinforcement learning-based joint channel allocation and relay selection framework for D2D communication. Initially, the channel allocation technique is proposed using the enhanced hunter prey optimization (EnHpo) algorithm. The adaptive weighting method is integrated with the traditional hunter-prey optimization in the design of the proposed EnHpo to improve the convergence rate and produce the global optimum solution with balanced local search and randomization phases. Here, the multi-objective fitness function based on factors like priority, bandwidth and transmission rate are considered for the optimal channel allocation. Followed by, the relay selection is devised using the deep reinforcement learning criteria based on the channel gain based on the bit error rate. Here, the relay sub-set selection in the using the deep reinforcement learning improves D2D communication efficiency. The performance evaluation of the proposed joint channel allocation and relay selection mechanism in terms of Average Residual Energy, Latency, Network Life Time, Packet Delivery Ratio, and Throughput acquired the values of 0.998, 2.709, 99.592, 0.999, and 23015 respectively. The maximum throughput estimated by the proposed method is 23015, which is 54.73%, 41.63%, 29.98%, and 8.00% superior outcome compared to the conventional DDPG Approach, Zigbee/WiFi Routing, Decode and Forward method, and Game-based Framework methods with 100 nodes.

Index Terms

Deep Reinforcement Learning

Channel Allocation

Joint Optimization

Device to Device Communication

Cooperative Networks

Enhanced Hunter Prey Optimization

Residual Energy

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