中圖分類號：TN929.5,；TN301.6 文獻標志碼：A DOI: 10.16157/j.issn.0258-7998.223278 中文引用格式： 彭昇,，趙建保，魏敏捷. 基于5G架構(gòu)超密集組網(wǎng)粒子群優(yōu)化算法改進[J]. 電子技術(shù)應(yīng)用,，2023,，49(1)：69-74. 英文引用格式： Peng Sheng，Zhao Jianbao,，Wei Minjie. Improvement of particle swarm algorithm based on ultra-dense networking under 5G architecture[J]. Application of Electronic Technique,，2023，49(1)：69-74.
Improvement of particle swarm algorithm based on ultra-dense networking under 5G architecture
Peng Sheng1,，Zhao Jianbao2,，Wei Minjie3
1.College of Electronic Information Engineering，Shanghai University of Electric Power,， Shanghai 201306,， China； 2.State Grid Information and Telecommunication Group Co.,， Ltd.,， Beijing 102200， China,； 3.College of Electrical Engineering，Shanghai University of Electric Power,， Shanghai 201306,， China
Abstract： With the development of mobile communication technology, traditional intelligent terminal devices cannot meet the rapidly growing massive data computing requirements. Mobile edge computing provides low-latency and flexible computing solutions for mobile users in the Internet of Things. Considering the limited computing resources on the edge server and the dynamic needs of users in the network, this paper proposes to allocate the transmit power to optimize the transmission energy consumption through the binary particle swarm optimization algorithm. Analyzing request offloading and resource scheduling as a dual decision-making problem, a new multi-objective optimization algorithm based on particle swarm optimization algorithm is proposed to solve the problem. The simulation results show that the binary particle swarm optimization algorithm can save transmission energy and has good convergence. The proposed new algorithm outperforms existing algorithms in terms of response rate and can maintain good performance in dynamic edge computing networks.
Key words : edge computing；resource optimization,；particle swarm optimization,；task offloading
邊緣計算資源調(diào)度的核心觀點是通過優(yōu)化移動邊緣計算來提高計算資源與能力從而滿足用戶的需求,。網(wǎng)絡(luò)運營商開始普遍構(gòu)建5G架構(gòu)的超密集組網(wǎng)（Ultra-Dense Network，UDN）多基站協(xié)同服務(wù)場景[3],。在UDN中通過部署宏基站（Macro-cell Base Station,MBS）與多個微基站（Small-cell Base Station,SBS）實現(xiàn)極高的頻率復(fù)用,，極大提高了覆蓋地區(qū)的系統(tǒng)容量與計算能力。
