Nehal Hamden, Ahmed Nasser, Mohamed Y. Selim, M. Elsabrouty
{"title":"Reinforcement Learning Based Technique for Interference Management in UAV Aided HetNets","authors":"Nehal Hamden, Ahmed Nasser, Mohamed Y. Selim, M. Elsabrouty","doi":"10.1109/JAC-ECC56395.2022.10043925","DOIUrl":null,"url":null,"abstract":"In this paper, we consider the sum rate maximization problem in a downlink unmanned aerial vehicles (UAV) assisted heterogeneous networks (HetNets) to mitigate existing interference. We propose to manage the interference by jointly optimizing the position of the UAV as well as power allocation. The proposed optimization problem is non-convex due to the non-linearity in the objective function and the constraints. We propose a near-optimal solution that breaks down the problem into two consecutive problems. First, we proposed a reinforcement learning-based technique to solve the UAV positioning problem. Then, a particle swarm optimization (PSO) based technique is applied to the power allocation problem. Simulation results demonstrate the efficiency of the proposed algorithm.","PeriodicalId":326002,"journal":{"name":"2022 10th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC)","volume":"796 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 10th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/JAC-ECC56395.2022.10043925","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In this paper, we consider the sum rate maximization problem in a downlink unmanned aerial vehicles (UAV) assisted heterogeneous networks (HetNets) to mitigate existing interference. We propose to manage the interference by jointly optimizing the position of the UAV as well as power allocation. The proposed optimization problem is non-convex due to the non-linearity in the objective function and the constraints. We propose a near-optimal solution that breaks down the problem into two consecutive problems. First, we proposed a reinforcement learning-based technique to solve the UAV positioning problem. Then, a particle swarm optimization (PSO) based technique is applied to the power allocation problem. Simulation results demonstrate the efficiency of the proposed algorithm.