Deep Reinforcement Learning for Distributed Uncoordinated Cognitive Radios Resource Allocation
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This paper presents a novel deep reinforcement learning-based resource allocation technique for the multi-agent environment presented by a cognitive radio network that coexists through underlay dynamic spectrum access (DSA) with a primary network. The resource allocation technique presented in this work is distributed, not requiring coordination with other agents. By ensuring convergence to equilibrium policies almost surely, the presented novel technique succeeds in addressing the challenge of a non-stationary multi-agent environment that results from the dynamic interaction between radios through the shared wireless environment. Simulation results show that in a finite learning time the presented technique is able to find policies that yield performance within 3 policy in nearly 70 reinforcement learning may not achieve convergence when used in a non-coordinated, coupled multi-radio scenario.
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