Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Autonomous solution methods for large-scale markov chains
Autonomous solution methods for large-scale markov chains
Queueing Networks and Markov Chains
Queueing Networks and Markov Chains
Allocating dynamic time-spectrum blocks in cognitive radio networks
Proceedings of the 8th ACM international symposium on Mobile ad hoc networking and computing
Efficient Discovery of Spectrum Opportunities with MAC-Layer Sensing in Cognitive Radio Networks
IEEE Transactions on Mobile Computing
Primary-prioritized Markov approach for dynamic spectrum allocation
IEEE Transactions on Wireless Communications
Modeling and analysis of opportunistic spectrum sharing with unreliable spectrum sensing
IEEE Transactions on Wireless Communications
Dynamic Spectrum Access and Management in Cognitive Radio Networks
Dynamic Spectrum Access and Management in Cognitive Radio Networks
Opportunistic spectrum scheduling for multiuser cognitive radio: a queueing analysis
IEEE Transactions on Wireless Communications
Channel fragmentation in dynamic spectrum access systems: a theoretical study
Proceedings of the ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Spectrum sharing in cognitive radio networks with imperfect sensing: A discrete-time Markov model
Computer Networks: The International Journal of Computer and Telecommunications Networking
Supporting demanding wireless applications with frequency-agile radios
NSDI'10 Proceedings of the 7th USENIX conference on Networked systems design and implementation
A non-selfish and distributed channel selection scheme for cognitive radio ad hoc networks
Proceedings of the 13th ACM international conference on Modeling, analysis, and simulation of wireless and mobile systems
IEEE/ACM Transactions on Networking (TON)
Full length article: Proactive channel access in dynamic spectrum networks
Physical Communication
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An efficient channel selection scheme in multi-user cognitive radio networks (CRN) is supposed to address two often conflicting objectives: enhancing the network-wide performance while satisfying the individual quality of service demands of cognitive radios (CRs). In this sense, best-fit channel selection (BFC) inspired from well-known classical bin-packing algorithms achieves better performance compared to the longest-idle time channel selection (LITC). BFC facilitates each CR, with the capability of primary channel idle time estimation, select the channel that is expected to be idle for sufficiently long duration for its traffic request. Unlike BFC, LITC favors the selection of the channel with the longest idle time although the channel is not needed and will not be used for such long duration by this CR. As a generalization of these two approaches, we introduce the p - selfish scheme in which a CR selects the longest channel with probability p. Hence, we also refer to p as degree of selfishness. In [1], we evaluate the performance of BFC and show that it improves performance of the CRN in terms of spectrum opportunity utilization and CR throughput, compared to the LITC. In this work, we present an analytic model for BFC using continuous-time Markov chains (CTMC). The performance improvement achieved by BFC is due the reduced spectrum fragmentation that is achieved by best-fit allocation. BFC can be considered as an implicit solution to spectrum fragmentation in time dimension. We study the CR performance in terms of spectrum opportunity utilization and probability of success under various degree of selfishness through the presented model and compare our results with the simulation results.