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<p>Device-to-device (D2D) communication is one among awful notable technologies of fifth-generation (5G) networks that uplift the network’s reliability and spectral efficiency. Interference management and sum rate maximization are the key aspects in D2D communication networks. Various researchers have given solutions to resolve the aforementioned issues based on convex optimization, machine learning, deep learning, game theory, and graph theory. State-of-the-art approaches are managed to mitigate interference and sum rate maximization. But, time complexity and the number of iterations taken by algorithms are not significantly optimized till now for the state-of-the-art approaches. So, motivated by these, this paper proposes a maximum matching algorithm for channel allocation with a faster convergence rate. The proposed approach divides the entire set of cellular users (CUs) and D2D groups into overlapping clusters based on channel gains. After clustering, the proposed approach utilizes the Kuhn-Munkres algorithm for the best channel allocation to the D2D groups within the same cluster. The performance of the proposed clustering-based matching approach is compared with the baseline approach considering sum rate and secrecy capacity parameters and found superior with fixed and varying numbers of CUs and D2D groups.</p>

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Next-gen D2D communication: efficient resource allocation with clustered combinatorial matching

  • 期刊论文

  • 发表日期：

    2026年8月5日

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  • Rajesh Gupta,
  • Sudeep Tanwar,
  • Sudhanshu Tyagi

摘要

Device-to-device (D2D) communication is one among awful notable technologies of fifth-generation (5G) networks that uplift the network’s reliability and spectral efficiency. Interference management and sum rate maximization are the key aspects in D2D communication networks. Various researchers have given solutions to resolve the aforementioned issues based on convex optimization, machine learning, deep learning, game theory, and graph theory. State-of-the-art approaches are managed to mitigate interference and sum rate maximization. But, time complexity and the number of iterations taken by algorithms are not significantly optimized till now for the state-of-the-art approaches. So, motivated by these, this paper proposes a maximum matching algorithm for channel allocation with a faster convergence rate. The proposed approach divides the entire set of cellular users (CUs) and D2D groups into overlapping clusters based on channel gains. After clustering, the proposed approach utilizes the Kuhn-Munkres algorithm for the best channel allocation to the D2D groups within the same cluster. The performance of the proposed clustering-based matching approach is compared with the baseline approach considering sum rate and secrecy capacity parameters and found superior with fixed and varying numbers of CUs and D2D groups.