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A Deep Reinforcement Learning Framework for Contention-Based Spectrum Sharing
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DOI：10.1109/JSAC.2021.3087254.png)
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Abstract
En 中文
The increasing number of wireless devices operating in unlicensed spectrum motivates the development of intelligent adaptive approaches to spectrum access. We consider decentralized contention-based medium access for base stations (BSs) operating on unlicensed shared spectrum, where each BS autonomously decides whether or not to transmit on a given resource. The contention decision attempts to maximize not its own downlink throughput, but rather a network-wide objective. We formulate this problem as a decentralized partially observable Markov decision process with a novel reward structure that provides long term proportional fairness in terms of throughput. We then introduce a two-stage Markov decision process in each time slot that uses information from spectrum sensing and reception quality to make a medium access decision. Finally, we incorporate these features into a distributed reinforcement learning framework for contention-based spectrum access. Our formulation provides decentralized inference, online adaptability and also caters to partial observability of the environment through recurrent Q-learning. Empirically, we find its maximization of the proportional fairness metric to be competitive with a genie-aided adaptive energy detection threshold, while being robust to channel fading and small contention windows.
Keywords:
Throughput
Interference
Reinforcement learning
Radio transmitters
Sensors
Signal to noise ratio
Markov processes
Spectrum sharing
distributed reinforcement learning
contention
medium access
proportional fairness
decentralized partially observable Markov decision process
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