{"title":"Intelligent UAV-Based Mobile Offloading: A Multi-Objective Optimization Approach","authors":"Farzad H. Panahi;Fereidoun H. Panahi","doi":"10.1109/TGCN.2024.3524003","DOIUrl":null,"url":null,"abstract":"We explore the use of an uncrewed aerial vehicle (UAV) flying on a circular path to offload mobile data from a ground base station (GBS) to enhance cellular network capacity. The UAV’s performance is constrained by battery life and energy-intensive radio frequency communications. To address this, we jointly optimize energy efficiency (EE) and spectrum efficiency (SE) by adjusting the UAV’s trajectory, speed, and minimum user throughput. The multi-objective optimization problem we propose is complex and non-convex, presenting substantial challenges in finding an optimal solution. We develop a tailored deep reinforcement learning (DRL) approach to address this specific problem. Simulations show that our method effectively balances EE and SE, enhancing UAV-based cellular offloading while maintaining robust performance, even in uncertain and dynamic conditions.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 3","pages":"900-909"},"PeriodicalIF":6.7000,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Green Communications and Networking","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10818501/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
We explore the use of an uncrewed aerial vehicle (UAV) flying on a circular path to offload mobile data from a ground base station (GBS) to enhance cellular network capacity. The UAV’s performance is constrained by battery life and energy-intensive radio frequency communications. To address this, we jointly optimize energy efficiency (EE) and spectrum efficiency (SE) by adjusting the UAV’s trajectory, speed, and minimum user throughput. The multi-objective optimization problem we propose is complex and non-convex, presenting substantial challenges in finding an optimal solution. We develop a tailored deep reinforcement learning (DRL) approach to address this specific problem. Simulations show that our method effectively balances EE and SE, enhancing UAV-based cellular offloading while maintaining robust performance, even in uncertain and dynamic conditions.