• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Opportunistic Spectrum Access with Discrete Feedback in Unknown and Dynamic Environmentツ」ツコA Multi-agent Learning Approach


Abstract

This article investigates the problem of opportunistic spectrum access in dynamic environment, in which the signal-to-noise ratio (SNR) is time-varying. Different from existing work on continuous feedback, we consider more practical scenarios in which the transmitter receives an Acknowledgment (ACK) if the received SNR is larger than the required threshold, and otherwise a Non-Acknowledgment (NACK). That is, the feedback is discrete. Several applications with different threshold values are also considered in this work. The channel selection problem is formulated as a non-cooperative game, and subsequently it is proved to be a potential game, which has at least one pure strategy Nash equilibrium. Following this, a multi-agent Q-learning algorithm is proposed to converge to Nash equilibria of the game. Furthermore, opportunistic spectrum access with multiple discrete feedbacks is also investigated. Finally, the simulation results verify that the proposed multi-agent Q-learning algorithm is applicable to both situations with binary feedback and multiple discrete feedbacks.


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Cite this article

[IEEE Style]
Zhan Gao, Junhong Chen and Yuhua Xu, "Opportunistic Spectrum Access with Discrete Feedback in Unknown and Dynamic Environmentツ」ツコA Multi-agent Learning Approach," KSII Transactions on Internet and Information Systems, vol. 9, no. 10, pp. 3867-3886, 2015. DOI: 10.3837/tiis.2015.10.006

[ACM Style]
Gao, Z., Chen, J., and Xu, Y. 2015. Opportunistic Spectrum Access with Discrete Feedback in Unknown and Dynamic Environmentツ」ツコA Multi-agent Learning Approach. KSII Transactions on Internet and Information Systems, 9, 10, (2015), 3867-3886. DOI: 10.3837/tiis.2015.10.006