首页|基于改进白鲸优化算法的D2D通信功率控制

基于改进白鲸优化算法的D2D通信功率控制

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D2D(Device-to-Device)通信作为未来移动通信网络的关键技术,为用户提供了直接通信的便利性和资源共享的高效性.然而,D2D通信的功率控制一直是影响通信质量和系统性能的关键问题.为解决这一问题,将精英反向学习、自适应权重两种策略引入到白鲸优化算法(Beluga Whale Optimization,BWO)中,并利用莱维飞行的随机步长策略来增加算法寻优的多样性,提出了基于改进白鲸优化算法的D2D通信功率控制方法.该方法利用最优解的信息引导搜索过程,可提高搜索效率和全局收敛,并能够有效提高通信效率和系统稳定性.为了验证所提出方法的有效性,开展了大量的数值仿真实验.结果显示,基于改进白鲸优化算法的D2D通信功率控制方法在增加系统吞吐量、减少干扰方面有显著的改善.同时,提出的算法相对于已有的算法有着更出色的收敛性与鲁棒性,在不同通信环境和参数设置下都能表现出更稳定的性能.
Power control of D2D communication based on improved Beluga Whale Optimization algorithm
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.

D2D communicationpower controlBeluga Whale Optimization algorithmelite reverse learningadaptive weightLevy flight

孙明、吕天宇

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齐齐哈尔大学 计算机与控制工程学院,黑龙江 齐齐哈尔 161006

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

2024

高师理科学刊
齐齐哈尔大学

高师理科学刊

影响因子:0.351
ISSN:1007-9831
年,卷(期):2024.44(4)
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