An anti-jamming stochastic game for cognitive radio networks

By Beibei Wang, Yongle Wu, K. Liu, T. Clancy. IEEE Journal on Selected Areas in Communications, 2011

294 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Survey on Machine-Learning Techniques in Cognitive Radios (2013, background): “The authors in [105] proposed a stochastic game framework for anti-jamming defense in cognitive radios.”
Jamming Attacks and Anti-Jamming Strategies in Wireless Networks: A Comprehensive Survey (2021, methodology): “Channel Re-selection Mechanisms [120]–[122] [123], [124] Ineffective against broadband and sweeping jamming attacks CRNs”
Deep Reinforcement Learning for Cyber Security (2019, background): “[147] developed a game-theoretic framework to battle against jamming in CRNs where each”
Edge Computing with Artificial Intelligence: A Machine Learning Perspective (2022, background): “[113] propose a stochastic game framework that maximizes the spectral efficiency throughput by minimax-Q learning, thereby gradually learning the optimal strategy.”
Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, unknown): “For example in [99], a jamming attack in a cognitive radio network used this algorithm”