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Learning-based sensing policies for multi-band flexible spectrum use, in particular cognitive radios operating in non-stationary radio environments are proposed. The proposed policies stem from the stochastic non-stationary restless multi-armed bandit formulation of opportunistic spectrum access. The non-stationary radio environment assumed in this paper is an appropriate model for a realistic cognitive...
In this paper, a spectrum sensing policy employing recency-based exploration is proposed for cognitive radio networks. We formulate the problem of finding a spectrum sensing policy for multiband dynamic spectrum access as a stochastic restless multiarmed bandit problem with stationary unknown reward distributions. In cognitive radio networks, the multiarmed bandit problem arises when deciding where...
In this paper a reinforcement learning-based distributed sensing policy is proposed for cognitive radio networks. The proposed sensing policy is controlled by a fusion center that employs action-value learning to focus the search for idle frequencies to those parts of the spectrum that persistently provide a high data rate. The fusion center learns the local sensing performances of the secondary users...
This paper addresses the problem of spectrum sharing in cognitive radios where secondary users (SUs) cooperatively sense the spectrum for identifying and accessing unoccupied spectrum bands. It is shown that spectrum sensing and sharing scenario can be modeled as a transferable utility (TU) cooperative game and that Vickrey-Clarke-Groves (VCG) auction mechanism can be used to allocate spectrum resources...
This paper focuses on the performance limitations for soft decision (SD) based cooperative spectrum sensing in cognitive radios. Cooperative sensing (CS) is formulated as a composite hypothesis problem where we assume that the distribution of the primary user signal is not known. CS suffers from the Bit Error Probability (BEP) wall phenomenon in the presence of reporting channel errors. If the BEP...
In this paper a distributed multiagent, multiband reinforcement learning based sensing policy for cognitive radio ad hoc networks is proposed. The proposed sensing policy employs secondary user (SU) collaboration through local interactions. The goal is to maximize the amount of available spectrum found for secondary use given a desired diversity order, i.e. a desired number of SUs sensing simultaneously...
Cognitive radios (CR) and dynamic spectrum access (DSA) attempt to exploit the underutilized radio spectrum by allowing secondary users to access the licensed frequencies in an opportunistic manner. In order to avoid collisions with the primary user the secondary users need to sense the spectrum, and to mitigate the effects of channel fading on sensing cooperative schemes have been proposed in the...
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