Emanuele De Santis, Alessandro Giuseppi, Antonio Pietrabissa, Michael Capponi, Francesco Delli Priscoli. Satellite Integration into 5G: Deep Reinforcement Learning for Network Selection. Machine Intelligence Research, vol. 19, no. 2, pp.127-137, 2022. https://doi.org/10.1007/s11633-022-1326-3
Citation: Emanuele De Santis, Alessandro Giuseppi, Antonio Pietrabissa, Michael Capponi, Francesco Delli Priscoli. Satellite Integration into 5G: Deep Reinforcement Learning for Network Selection. Machine Intelligence Research, vol. 19, no. 2, pp.127-137, 2022. https://doi.org/10.1007/s11633-022-1326-3

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

doi: 10.1007/s11633-022-1326-3
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  • Author Bio:

    Emanuele De Santis received the B. Sc. degree in automatic control and M. Sc. degree in engineering in computer science with specialization in communication networks control from Sapienza University of Rome, Italy in 2017 and 2019, where he is currently a Ph. D. degree candidate in automatic control. He participated in the H2020 projects 5G-ALLSTAR and 5G-Solutions and in the European Space Agency (ESA) project ARIES. He is a student member of IEEE. His research interests include power and communication network control, artificial intelligence, and optimal control. E-mail: edesantis@diag.uniroma1.it (Corresponding author) ORCID iD: 0000-0003-1011-9737

    Alessandro Giuseppi received the B. Sc. degree in computer and automation engineering, the M. Sc. degree in control engineering and the Ph. D. degree in automatica from University of Rome La Sapienza, Italy in 2014, 2016 and 2019, respectively, where he is currently a postdoctoral researcher in automatic control. Since 2016, he has participated in five European and national research projects. He is a member of IEEE. His research interests include network control and intelligent systems. E-mail: giuseppi@diag.uniroma1.it ORCID iD: 0000-0001-5503-8506

    Antonio Pietrabissa received the M. Sc. degree in electronics engineering and the Ph. D. degree in systems engineering from Sapienza University of Rome, Italy in 2000 and 2004, respectively. He is an associate professor at Sapienza University of Rome, Italy. He has participated in about 20 European and national research projects. He is a senior member of IEEE. His research interests include the application of systems and control theory to the analysis and control of networks. E-mail: pietrabissa@diag.uniroma1.it ORCID iD: 0000-0003-0188-3346

    Michael Capponi received the M. Sc. degree in communication networks control from Sapienza University of Rome, Italy in 2020. Now, he works in a company in the field of computer science.His research interests include reinforcement learning applications to communication networks. E-mail: michaelcapponi96@gmail.com ORCID iD: 0000-0002-5610-9422

    Francesco Delli Priscoli received the M. Sc. degree in electronics engineering and the Ph. D. degree in systems engineering from University of Rome, Italy in 1986 and 1991, respectively. From 1986 to 1991, he was with Telespazio, Italy. Since 1991, he has been with University of Rome, Italy, where, at present, he is a full professor of automatic control, control of autonomous multiagent systems, and control of communication and energy networks. He is an Associate Editor of Control Engineering Practice and a Member of the IFAC Technical Committee on Networked Systems. He was/is the Scientific Responsible with University of Rome, for 40 projects funded by the European Union and by the European Space Agency. He is a member of IEEE. His research interests include closed-loop multiagent learning techniques in advanced communication and energy networks. E-mail: dellipriscoli@diag.uniroma1.it ORCID iD: 0000-0001-6140-3661

  • Received Date: 2021-11-22
  • Accepted Date: 2022-03-01
  • Publish Date: 2022-04-01
  • 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 balancing. In particular, the DQN algorithm has been tested against classic reinforcement learning one and other baseline approaches.

     

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