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    MAC LAYER RESOURCE ALLOCATION IN

    COGNITIVE RADIO NETWORKS

The explosive growth in wireless technologies has escalated the demand for the radio spectrum. Per contra, recent studies exhibit that spectrum usage is concentrated on certain portions of the spectrum while a significant amount of the spectrum remains underutilized. This situation implies that there is a problem with the spectrum management and allocation methodology, rather than the true scarcity of the spectrum. Dynamic Spectrum Access (DSA) methods enable the devices to opportunistically access the licensed frequency bands, and thereby enhance the utilization of the existing wireless spectrum. Cognitive radios are intelligent devices that can sense and autonomously reason about their environment and adapt their communication parameters in response to the network and user demands. They are the next evolution of adaptive/aware radios through the addition of a layer of intelligence providing the ability to realize the DSA concept.

The nodes in a cognitive radio network can be classified as primary and secondary users. A primary user is a licensed user that has paid for the spectrum and hence has exclusive rights to access it. A secondary user is an unlicensed user that can utilize the temporarily unused licensed spectrum bands opportunistically, as long as it vacates them as soon as a primary user appears. Primary users are oblivious to the secondary users and do not have cognitive capabilities.

FCC has proposed the Interference Temperature Model, which prescribes true coexistence between licensed and unlicensed users. In this model, secondary users are allowed to simultaneously operate on the same frequencies as the primary users provided that they can quantify and bound the additional interference exposed to the primary users. The interference temperature threshold for a particular frequency quantifies the maximum aggregate interference that can occur at a primary node in that frequency.

As part of my research, I formulated throughput and delay optimal schedulers for cognitive radio networks under interference temperature constraints. Furthermore, I proposed two Genetic Algorithm (GA) based suboptimal schedulers that yield close performance to the optimal schedulers. In addition, I extended the previous research problem to accommodate conventional physical layer sensing mechanisms rather than the interference temperature concept, which spurred a lot of debate since its inception and received both positive and negative feedback. I also proposed (weighted) max-min and proportionally fair schedulers as well as a spectrum switching delay-aweas part of the general resource allocation framework. Moreover, I designed a spectrum switching-delay aware scheduler and a scheduler that maximizes the number of satisfied users in cognitive radio networks. Currently, I am working on the design of graph algorithms for these throughput optimal and fair scheduling problems.