{"title":"Deep Reinforcement Learning Framework for Joint Resource Allocation in Heterogeneous Networks","authors":"Yong Zhang, Canping Kang, Yinglei Teng, Sisi Li, Weijun Zheng, Jinghui Fang","doi":"10.1109/VTCFall.2019.8891448","DOIUrl":null,"url":null,"abstract":"In this study, a deep reinforcement learning (DRL) method was employed to solve the joint optimization problem for user association, resource allocation, and power allocation in heterogeneous networks (HetNets), which is an NP-hard problem. Existing studies have taken various optimization objectives into account. The heterogeneous network-deep-Q- network frame-work (HetDQN) is proposed to solve this type of optimization problem in HetNets. Based on maximum spectral efficiency, we designed a 6- layer deep neural network. The state space, objective function, and reward function are presented. In comparison with the existing solution, HetDQN can achieve a higher spectral efficiency. The simulation results revealed that HetDQN has better performance in term of convergence.","PeriodicalId":6713,"journal":{"name":"2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)","volume":"24 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VTCFall.2019.8891448","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
In this study, a deep reinforcement learning (DRL) method was employed to solve the joint optimization problem for user association, resource allocation, and power allocation in heterogeneous networks (HetNets), which is an NP-hard problem. Existing studies have taken various optimization objectives into account. The heterogeneous network-deep-Q- network frame-work (HetDQN) is proposed to solve this type of optimization problem in HetNets. Based on maximum spectral efficiency, we designed a 6- layer deep neural network. The state space, objective function, and reward function are presented. In comparison with the existing solution, HetDQN can achieve a higher spectral efficiency. The simulation results revealed that HetDQN has better performance in term of convergence.