Open Access

ARTICLE

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

by Owais Muhammad¹, Hong Jiang^1,*, 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:

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

Received 15 April 2023; Accepted 21 June 2023; Issue published 11 September 2023

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.

---

Keywords

---