摘要
Abstract
Device-to-Device(D2D)communication,as a critical technology in future mobile communication networks,provides users with the convenience of direct communication and the efficiency of resource sharing.However,the power control issue in D2D communication has consistently been a key challenge affecting communication quality and system performance.In order to address this challenge,two strategies,elite reverse learning and adaptive weighting,are introduced into the Beluga Whale Optimization(BWO)algorithm.Additionally,a random step-size strategy using Levy flights is employed to enhance the diversity of the optimization process,and a D2D communication power control method based on an improved BWO algorithm is proposed.The proposed method utilizes information from optimal solutions to guide the search process,improving search efficiency and global convergence,thereby effectively enhancing communication efficiency and system stability.To validate the effectiveness of the proposed method,extensive numerical simulation experiments were conducted.The results demonstrate significant improvements in increasing system throughput and reducing interference achieved by the D2D communication power control method based on the improved BWO algorithm.Moreover,the proposed algorithm exhibits superior convergence and robustness compared to existing methods,demonstrating stable performance across different communication environments and parameter settings.关键词
D2D通信/功率控制/白鲸优化算法/精英反向学习/自适应权重/莱维飞行Key words
D2D communication/power control/Beluga Whale Optimization algorithm/elite reverse learning/adaptive weight/Levy flight分类
信息技术与安全科学
