An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
EURASIP Journal on Wireless Communications and Networking - Cognitive Radio and Dynamic Spectrum Sharing Systems
Optimality of myopic sensing in multichannel opportunistic access
IEEE Transactions on Information Theory
On myopic sensing for multi-channel opportunistic access: structure, optimality, and performance
IEEE Transactions on Wireless Communications - Part 2
IEEE Transactions on Information Theory
Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework
IEEE Journal on Selected Areas in Communications
Dynamic multichannel access with imperfect channel state detection
IEEE Transactions on Signal Processing
Sequential and cooperative sensing for multi-channel cognitive radios
IEEE Transactions on Signal Processing
Throughput-efficient dynamic coalition formation in distributed cognitive radio networks
EURASIP Journal on Wireless Communications and Networking
Dynamic channel, rate selection and scheduling for white spaces
Proceedings of the Seventh COnference on emerging Networking EXperiments and Technologies
Access Control Engine with Dynamic Priority Resource Allocation for Cognitive Radio Networks
Wireless Personal Communications: An International Journal
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We study the problem of dynamic spectrum sensing and access in cognitive radio systems as a partially observed Markov decision process (POMDP). A group of cognitive users cooperatively tries to exploit vacancies in primary (licensed) channels whose occupancies follow a Markovian evolution. We first consider the scenario where the cognitive users have perfect knowledge of the distribution of the signals they receive from the primary users. For this problem, we obtain a greedy channel selection and access policy that maximizes the instantaneous reward, while satisfying a constraint on the probability of interfering with licensed transmissions. We also derive an analytical universal upper bound on the performance of the optimal policy. Through simulation, we show that our scheme achieves good performance relative to the upper bound and improved performance relative to an existing scheme. We then consider the more practical scenario where the exact distribution of the signal from the primary is unknown. We assume a parametric model for the distribution and develop an algorithm that can learn the true distribution, still guaranteeing the constraint on the interference probability. We show that this algorithm outperforms the naive design that assumes a worst case value for the parameter. We also provide a proof for the convergence of the learning algorithm.