RFRL Gym: A Reinforcement Learning Testbed for Cognitive Radio Applications

• Published in: arXiv.org
• Main Authors: Rosen, Daniel, Rochez, Illa, McIrvin, Caleb, Lee, Joshua, D'Alessandro, Kevin, Wiecek, Max, Hoang, Nhan, Saffarini, Ramzy, Philips, Sam, Jones, Vanessa, Ivey, Will, Harris-Smart, Zavier, Harris-Smart, Zavion, Chin, Zayden, Johnson, Amos, Jones, Alyse M, Headley, William C
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 20.12.2023
• Subjects: Algorithms; Cognitive radio; Computer Science - Artificial Intelligence; Computer Science - Learning; Computer Science - Networking and Internet Architecture; Jamming; Machine learning; Military communications; Radio frequency; Test stands; Wireless communication systems; Wireless communications
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2401.05406

Radio Frequency Reinforcement Learning (RFRL) is anticipated to be a widely applicable technology in the next generation of wireless communication systems, particularly 6G and next-gen military communications. Given this, our research is focused on developing a tool to promote the development of RFRL techniques that leverage spectrum sensing. In particular, the tool was designed to address two cognitive radio applications, specifically dynamic spectrum access and jamming. In order to train and test reinforcement learning (RL) algorithms for these applications, a simulation environment is necessary to simulate the conditions that an agent will encounter within the Radio Frequency (RF) spectrum. In this paper, such an environment has been developed, herein referred to as the RFRL Gym. Through the RFRL Gym, users can design their own scenarios to model what an RL agent may encounter within the RF spectrum as well as experiment with different spectrum sensing techniques. Additionally, the RFRL Gym is a subclass of OpenAI gym, enabling the use of third-party ML/RL Libraries. We plan to open-source this codebase to enable other researchers to utilize the RFRL Gym to test their own scenarios and RL algorithms, ultimately leading to the advancement of RL research in the wireless communications domain. This paper describes in further detail the components of the Gym, results from example scenarios, and plans for future additions. Index Terms-machine learning, reinforcement learning, wireless communications, dynamic spectrum access, OpenAI gym