Experimental Analysis of Reinforcement Learning Techniques for Spectrum Sharing Radar

• URL: http://arxiv.org/abs/2001.01799v2
• Date: Fri, 13 Mar 2020 23:24:44 GMT
• Title: Experimental Analysis of Reinforcement Learning Techniques for Spectrum Sharing Radar
• Authors: Charles E. Thornton, R. Michael Buehrer, Anthony F. Martone, Kelly D. Sherbondy
• Abstract summary: We describe a framework for the application of Reinforcement (RL) control to a radar system that operates in a congested spectral setting. We compare the utility of several RL algorithms through a discussion of experiments performed on Commercial off-the-shelf (COTS) hardware. Each RL technique is evaluated in terms of convergence, radar detection performance achieved in a congested spectral environment, and the ability to share 100MHz spectrum with an uncooperative communications system.
• Score: 8.852345851445829
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we first describe a framework for the application of Reinforcement Learning (RL) control to a radar system that operates in a congested spectral setting. We then compare the utility of several RL algorithms through a discussion of experiments performed on Commercial off-the-shelf (COTS) hardware. Each RL technique is evaluated in terms of convergence, radar detection performance achieved in a congested spectral environment, and the ability to share 100MHz spectrum with an uncooperative communications system. We examine policy iteration, which solves an environment posed as a Markov Decision Process (MDP) by directly solving for a stochastic mapping between environmental states and radar waveforms, as well as Deep RL techniques, which utilize a form of Q-Learning to approximate a parameterized function that is used by the radar to select optimal actions. We show that RL techniques are beneficial over a Sense-and-Avoid (SAA) scheme and discuss the conditions under which each approach is most effective.

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