Technical Note: \cal Q-Learning
Machine Learning
Distributed Detection and Data Fusion
Distributed Detection and Data Fusion
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
CRAHNs: Cognitive radio ad hoc networks
Ad Hoc Networks
Probability-based combination for cooperative spectrum sensing
IEEE Transactions on Communications
Optimal spectrum sensing framework for cognitive radio networks
IEEE Transactions on Wireless Communications
Soft Combination and Detection for Cooperative Spectrum Sensing in Cognitive Radio Networks
IEEE Transactions on Wireless Communications - Part 2
A survey of common control channel design in cognitive radio networks
Physical Communication
Cooperative spectrum sensing in cognitive radio networks: A survey
Physical Communication
| Hi-index | 0.00 |
Spectrum sensing is a fundamental function in cognitive radio networks for detecting the presence of primary users in licensed bands. The detection performance may be considerably compromised due to multipath fading and shadowing. To resolve this issue, cooperative sensing is an effective approach to combat channel impairments by cooperation of secondary users. This approach, however, incurs overhead such as delay for reporting local decisions and the increase of control traffic. In this paper, a reinforcement learning-based cooperative sensing (RLCS) method is proposed to address the cooperation overhead problem and improve cooperative gain in cognitive radio ad hoc networks. The proposed algorithm is proven to converge and capable of (1) finding the optimal set of cooperating neighbors with minimum control traffic, (2) minimizings the overall cooperative sensing delay, (3) selecting independent users for cooperation under correlated shadowing, and (4) excluding unreliable users and data from cooperation. Simulation results show that the RLCS method reduces the overhead of cooperative sensing while effectively improving the detection performance to combat correlated shadowing. Moreover, it adapts to environmental change and maintains comparable performance under the impact of primary user activity, user movement, user reliability, and control channel fading.