Abstract
In cognitive radios systems, the sparse assigned frequency bands are opened to secondary users, provided that the aggregated interferences induced by the secondary transmitters on the primary receivers are negligible. Cognitive radios are established in two steps: the radios firstly sense the available frequency bands and secondly communicate using these bands. In this article, we propose two decentralized resource allocation Q-learning algorithms: the first one is used to share the sensing time among the cognitive radios in a way that maximize the throughputs of the radios. The second one is used to allocate the cognitive radio powers in a way that maximizes the signal on interference-plus-noise ratio (SINR) at the secondary receivers while meeting the primary protection constraint. Numerical results show the convergence of the proposed algorithms and allow the discussion of the exploration strategy, the choice of the cost function and the frequency of execution of each algorithm.
Highlights
The scarcity of available radio spectrum frequencies, densely allocated by the regulators, represents a major bottleneck in the deployment of new wireless services
The assigned frequency bands are opened to secondary users, provided that interference induced on the primary licensees is negligible
The first one was used to solve the problem of the allocation of the sensing durations in a cooperative cognitive network in a way that maximize the throughputs of the cognitive radios
Summary
The scarcity of available radio spectrum frequencies, densely allocated by the regulators, represents a major bottleneck in the deployment of new wireless services. In [10], a decentralized power allocation Q-learning algorithm is proposed to protect the primary network from harmful aggregated interference. This article aims to illustrate the potential of Q-learning for cognitive radio systems For this purpose two decentralized Q-learning algorithm are presented to solve the allocation problems that appear during the sensing phase on the one hand and during the communication phase on the other hand. The second algorithm allows to allocate the secondary user powers in a way that maximize the signal on interference-plus-noise ratio (SINR) at the secondary receivers while meeting the primary protection constraint. Many traditional multi-agent reinforcement learning algorithms like fictitious play and Nash-Q learning cannot be used [12], which justifies the use of multi-agent Q-learning in this article to solve the sensing time and power allocation problems.
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