Convex Optimization
Allocating dynamic time-spectrum blocks in cognitive radio networks
Proceedings of the 8th ACM international symposium on Mobile ad hoc networking and computing
Understanding the power of distributed coordination for dynamic spectrum management
Mobile Networks and Applications
Optimal Transmission Strategies for Dynamic Spectrum Access in Cognitive Radio Networks
IEEE Transactions on Mobile Computing
On cognitive radio networks with opportunistic power control strategies in fading channels
IEEE Transactions on Wireless Communications
Joint rate and power allocation for cognitive radios in dynamic spectrum access environment
IEEE Transactions on Wireless Communications - Part 2
A survey on spectrum management in cognitive radio networks
IEEE Communications Magazine
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
Spectrum Sharing for Multi-Hop Networking with Cognitive Radios
IEEE Journal on Selected Areas in Communications
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Cognitive radio (CR) technology has great potential to alleviate spectrum scarcity in wireless communications. It allows secondary users (SUs) to opportunistically access spectrum licensed by primary users (PUs) while protecting PU activity. The protection of the PUs is central to the adoption of this technology since no PU would accommodate SU access to its own detriment. In this paper, we consider an SUthat must protectmultiple PUs simultaneously. We focus on the PU packet collision probability as the protection metric. The PUs are unslotted and may have different idle/busy time distributions and protection requirements. Under general idle time distributions, we determine the form of the SU optimal access policy and identify two special cases for which the computation of the optimal policy is significantly reduced. We also present a simple algorithm to determine these policies using principles of convex optimization theory. We then derive the optimal policy for the same system when an SU has extra "side information" on PU activity. We evaluate the performance of these policies through simulation.