Multi-Objective Reinforcement Learning for Cognitive Radio Based Satellite Communications

Ferreira, Paulo; Paffenroth, Randy; Wyglinski, Alexander; Hackett, Timothy; Bilen, Sven; Reinhart, Richard; Mortensen, Dale John

NASA NTRS · 20170007271

Multi-Objective Reinforcement Learning for Cognitive Radio Based Satellite Communications

Ferreira, Paulo·Paffenroth, Randy·Wyglinski, Alexander·Hackett, Timothy·Bilen, Sven·Reinhart, Richard·Mortensen, Dale John

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Abstract

Previous research on cognitive radios has addressed the performance of various machine learning and optimization techniques for decision making of terrestrial link properties. In this paper, we present our recent investigations with respect to reinforcement learning that potentially can be employed by future cognitive radios installed onboard satellite communications systems specifically tasked with radio resource management. This work analyzes the performance of learning, reasoning, and decision making while considering multiple objectives for time-varying communications channels, as well as different crosslayer requirements. Based on the urgent demand for increased bandwidth, which is being addressed by the next generation of high-throughput satellites, the performance of cognitive radio is assessed considering links between a geostationary satellite and a fixed ground station operating at Ka-band (26 GHz). Simulation results show multiple objective performance improvements of more than 3:5 times for clear sky conditions and 6:8 times for rain conditions.

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Ferreira, Paulo, Paffenroth, Randy, Wyglinski, Alexander, Hackett, Timothy, Bilen, Sven, Reinhart, Richard, Mortensen, Dale John. 2016-10-18. Multi-Objective Reinforcement Learning for Cognitive Radio Based Satellite Communications. https://ntrs.nasa.gov/citations/20170007271

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