A Reinforcement Learning Approach to Dynamic Spectrum Access in Internet-of-Things Networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

To support wireless communication traffic of Internet-of-Things (IoT) systems in terms of massive connectivity, dynamic spectrum access (DSA) is important issue. This paper proposes spectrum sensor-aided DSA system based on a reinforcement learning (RL) algorithm that aims at efficient spectrum usage for IoT network over the incumbent network. Due to small-form-factor of IoT devices, they do not have spectrum sensing capability. To support DSA of IoT devices, we introduce sensor-aided DSA system that enhances spatial spectrum reusability by means of RL algorithm. With the RL algorithm, proposed DSA system provides self-organizing feature for massive number of IoT devices. We show that the performance of proposed RL based DSA system in various densities of IoT devices utilizing slotted ALOHA protocol that has spectrum access probability learned by proposed DSA system. We also present the performance of proposed RL based DSA system surpass that of distributed Carrier Sensing Multiple Access with Collision Avoidance (CSMA/CA) protocol for channel access coordination. We also present the consistent performance of incumbent user when the IoT devices access to the spectrum band with learned spectrum access probability.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Communications, ICC 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538680889
DOIs
Publication statusPublished - 2019 May
Event2019 IEEE International Conference on Communications, ICC 2019 - Shanghai, China
Duration: 2019 May 202019 May 24

Publication series

NameIEEE International Conference on Communications
Volume2019-May
ISSN (Print)1550-3607

Conference

Conference2019 IEEE International Conference on Communications, ICC 2019
CountryChina
CityShanghai
Period19/5/2019/5/24

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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  • Cite this

    Cha, H., & Kim, S. L. (2019). A Reinforcement Learning Approach to Dynamic Spectrum Access in Internet-of-Things Networks. In 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings [8762091] (IEEE International Conference on Communications; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICC.2019.8762091