Dynamic Spectrum Allocation for Cognitive Radio Networks: A Comprehensive Optimization Approach
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In Cognitive Radio Networks (CRNs), the role of the Medium Access Control (MAC) layer is very important since it enables Secondary Users (SUs) to access the spectrum without affecting Primary Users' (PUs) communications. SUs' and PUs' geometry has an effect on the performance of the spectrum sharing algorithms. Also, SUs' mobility changes the topology of the network as well as interference between the PUs and SUs. The scenario of multiuser multichannel CRNs introduces new challenges such as co-channel interference. Consequently, the power budget should be allocated to the SUs subject to specific constraints. Hence, different SUs will have different power and interference limits depending on the activity of PUs and on which SUs will be causing co-channel interference to each other. In addition, enabling Energy Harvesting (EH) in CRNs is promising to extend their lifetime so that the hybrid interweave/underlay access scheme is adopted, which means that SUs can access the active and non-active PU bands. In this thesis, I propose new optimal and suboptimal Dynamic Spectrum Allocation (DSA) algorithms that employ an interweave/underlay access scheme. I also study the impact of the following factors: mobility of the SUs, spectrum mobility, the Primary Exclusive Regions (PERs), the geographical locations of the nodes, connectivity of SUs, correlated shadow fading, and the activity of both PUs and SUs. A cross-layer approach is adopted in order to benefit from the information of the other layers. Moreover, to increase both the energy efficiency and the spectrum efficiency, I also propose a novel algorithm that enables SUs to harvest energy with minimal impact on their spectrum access performance. The algorithm allows SUs to participate in making decisions regarding their operating mode. Also, the algorithm ensures that the energy level in CRN cannot be lower than a specific threshold. Furthermore, I propose different optimal and suboptimal algorithms that optimize the power allocation among SUs. The objective is to maximize the Spectral Efficiency (SE) while respecting the power budget along with the other constraints. Extensive simulations have been conducted and the results are presented for all of the proposed algorithms.