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Optimizing Power Allocation for D2D Communication with URLLC under Rician Fading Channel: A Learning-to-Optimize Approach

Owais Muhammad1, Hong Jiang1,*, Mushtaq Muhammad Umer1, Bilal Muhammad2, Naeem Muhammad Ahtsam3

1 School of Information Engineering, Southwest University of Science and Technology, Mianyang, 621010, China
2 School of Software Engineering, Northeastern University, Shenyang, 110167, China
3 School of Information and Software Engineering, University of Electronic Sciences and Technology, Chengdu, 610000, China

* Corresponding Author: Hong Jiang. Email: email

Intelligent Automation & Soft Computing 2023, 37(3), 3193-3212. https://doi.org/10.32604/iasc.2023.041232

Abstract

To meet the high-performance requirements of fifth-generation (5G) and sixth-generation (6G) wireless networks, in particular, ultra-reliable and low-latency communication (URLLC) is considered to be one of the most important communication scenarios in a wireless network. In this paper, we consider the effects of the Rician fading channel on the performance of cooperative device-to-device (D2D) communication with URLLC. For better performance, we maximize and examine the system’s minimal rate of D2D communication. Due to the interference in D2D communication, the problem of maximizing the minimum rate becomes non-convex and difficult to solve. To solve this problem, a learning-to-optimize-based algorithm is proposed to find the optimal power allocation. The conventional branch and bound (BB) algorithm are used to learn the optimal pruning policy with supervised learning. Ensemble learning is used to train the multiple classifiers. To address the imbalanced problem, we used the supervised undersampling technique. Comparisons are made with the conventional BB algorithm and the heuristic algorithm. The outcome of the simulation demonstrates a notable performance improvement in power consumption. The proposed algorithm has significantly low computational complexity and runs faster as compared to the conventional BB algorithm and a heuristic algorithm.

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APA Style
Muhammad, O., Jiang, H., Umer, M.M., Muhammad, B., Ahtsam, N.M. (2023). Optimizing power allocation for D2D communication with URLLC under rician fading channel: A learning-to-optimize approach. Intelligent Automation & Soft Computing, 37(3), 3193-3212. https://doi.org/10.32604/iasc.2023.041232
Vancouver Style
Muhammad O, Jiang H, Umer MM, Muhammad B, Ahtsam NM. Optimizing power allocation for D2D communication with URLLC under rician fading channel: A learning-to-optimize approach. Intell Automat Soft Comput . 2023;37(3):3193-3212 https://doi.org/10.32604/iasc.2023.041232
IEEE Style
O. Muhammad, H. Jiang, M.M. Umer, B. Muhammad, and N.M. Ahtsam "Optimizing Power Allocation for D2D Communication with URLLC under Rician Fading Channel: A Learning-to-Optimize Approach," Intell. Automat. Soft Comput. , vol. 37, no. 3, pp. 3193-3212. 2023. https://doi.org/10.32604/iasc.2023.041232



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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