Technical Note: \cal Q-Learning
Machine Learning
Distributed Detection and Data Fusion
Distributed Detection and Data Fusion
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
A Near Optimal Policy for Channel Allocation in Cognitive Radio
Recent Advances in Reinforcement Learning
Autocorrelation-based decentralized sequential detection of OFDM signals in cognitive radios
IEEE Transactions on Signal Processing
Collaborative cyclostationary spectrum sensing for cognitive radio systems
IEEE Transactions on Signal Processing
Dynamic multichannel access with imperfect channel state detection
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Opportunistic Spectrum Access via Periodic Channel Sensing
IEEE Transactions on Signal Processing
On myopic sensing for multi-channel opportunistic access: structure, optimality, and performance
IEEE Transactions on Wireless Communications - Part 2
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework
IEEE Journal on Selected Areas in Communications
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This paper introduces a machine learning based collaborative multi-band spectrum sensing policy for cognitive radios. The proposed sensing policy guides secondary users to focus the search of unused radio spectrum to those frequencies that persistently provide them high data rate. The proposed policy is based on machine learning, which makes it adaptive with the temporally and spatially varying radio spectrum. Furthermore, there is no need for dynamic modeling of the primary activity since it is implicitly learned over time. Energy efficiency is achieved by minimizing the number of assigned sensors per each subband under a constraint on miss detection probability. It is important to control the missed detections because they cause collisions with primary transmissions and lead to retransmissions at both the primary and secondary user. Simulations show that the proposed machine learning based sensing policy improves the overall throughput of the secondary network and improves the energy efficiency while controlling the miss detection probability.