Satellite Integration into 5G:Deep Reinforcement Learning for Network Selection

作     者：Emanuele De Santis Alessandro Giuseppi Antonio Pietrabissa Michael Capponi Francesco Delli Priscoli Emanuele De Santis;Alessandro Giuseppi;Antonio Pietrabissa;Michael Capponi;Francesco Delli Priscoli

作者机构：Department of ComputerControl and Management Engineering“Antonio Ruberti”University of Rome La SapienzaRome 00185Italy 

出 版 物：《Machine Intelligence Research》 (机器智能研究（英文版）)

年 卷 期：2022年第19卷第2期

页      面：127-137页

核心收录：

学科分类：0710[理学-生物学] 0810[工学-信息与通信工程] 080904[工学-电磁场与微波技术] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the European Commission in the framework of the H2020 EU-Korea project 5GALLSTAR(5G Agi Le and f Lexible integration of Sa Tellite And cellula R www.5g-allstar.eu)(No.815323) 

主　　题：Network selection HetNet deep reinforcement learning deep-Q-network(DQN) 5G communications 

摘      要：This paper proposes a deep-Q-network(DQN) controller for network selection and adaptive resource allocation in heterogeneous networks, developed on the ground of a Markov decision process(MDP) model of the problem. Network selection is an enabling technology for multi-connectivity, one of the core functionalities of 5G. For this reason, the present work considers a realistic network model that takes into account path-loss models and intra-RAT(radio access technology) interference. Numerical simulations validate the proposed approach and show the improvements achieved in terms of connection acceptance, resource allocation, and load *** particular, the DQN algorithm has been tested against classic reinforcement learning one and other baseline approaches.