{"title":"基于智能无人机的移动卸载:多目标优化方法","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":"{\"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}","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}
Intelligent UAV-Based Mobile Offloading: A Multi-Objective Optimization Approach
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.