Learning for Robust Routing Based on Stochastic Game in Cognitive Radio Networks



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IEEE Transactions on Communications


This paper studies the problem of spectrum-aware routing in a multi-hop, multi-channel cognitive radio network when malicious nodes in the secondary network attempt to block the path with mixed attacks. Based on the location and time-variant path delay information, we model the path discovery process as a non-cooperative stochastic game. By exploiting the structure of the underlying Markov Decision Process, we decompose the stochastic routing game into a series of stage games. For each stage game, we propose a distributed strategy learning mechanism based on stochastic fictitious play to learn the equilibrium strategies of joint relay-channel selection in the condition of both limited information exchange and potential routing-toward-primary attacks. We also introduce a trustworthiness evaluation mechanism based on a multi-arm bandit process for normal users to avoid relaying to the sink-hole attackers. Simulation results show that without the need of information flooding, the proposed algorithm is efficient in bypassing the malicious nodes with mixed attacks.



Cognitive radio networks, spectrum-aware routing, stochastic game, two timescale learning


Copyright 2016 IEEE Transactions on Wireless Communications. This is a pre-print version of a published paper that is available at: https://ieeexplore.ieee.org/abstract/document/8272421. Recommended citation: Wang, Wenbo, Andres Kwasinski, Dusit Niyato, and Zhu Han. "Learning for robust routing based on stochastic game in cognitive radio networks." IEEE Transactions on Communications 66, no. 6 (2018): 2588-2602. This item has been deposited in accordance with publisher copyright and licensing terms and with the author's permission.