Distributed Deep Reinforcement Learning for Collaborative Spectrum Sharing. (arXiv:2104.02059v1 [eess.SP])

Spectrum sharing among users is a fundamental problem in the management of
any wireless network. In this paper, we discuss the problem of distributed
spectrum collaboration without central management under general unknown
channels. Since the cost of communication, coordination and control is rapidly
increasing with the number of devices and the expanding bandwidth used there is
an obvious need to develop distributed techniques for spectrum collaboration
where no explicit signaling is used. In this paper, we combine game-theoretic
insights with deep Q-learning to provide a novel asymptotically optimal
solution to the spectrum collaboration problem. We propose a deterministic
distributed deep reinforcement learning(D3RL) mechanism using a deep Q-network
(DQN). It chooses the channels using the Q-values and the channel loads while
limiting the options available to the user to a few channels with the highest
Q-values and among those, it selects the least loaded channel. Using insights
from both game theory and combinatorial optimization we show that this
technique is asymptotically optimal for large overloaded networks. The selected
channel and the outcome of the successful transmission are fed back into the
learning of the deep Q-network to incorporate it into the learning of the
Q-values. We also analyzed performance to understand the behavior of D3RL in

Source: https://arxiv.org/abs/2104.02059


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