Efficient Resource Allocation in Cognitive Radio Network

Abstract

Cognitive radio network based on Device to Device and the heterogeneous network has attracted wide attention as they can resolve spectrum shortage problems and efficiently utilizes spectrum resources. However, resource allocation considering stochastic behavior has not been considered in any work. In this thesis, the proposed work aimed for maximizing the throughput of the overall network considering multiple users under the umbrella of the Cognitive radio network assisted by amplify and forward relay. The constraints are treated as chance constraints with a probability of satisfaction in them, which leads to a nonconvex Mixed integer nonlinear problem which is an NP-Hard problem. To solve this problem an exhaustive search solution for optimal results is required. However, the computational burden always increases with the user equipment. Therefore, to obtain an optimal solution with having low computational burden, Outer Approximation Algorithm is utilized in this research. To evaluate the desired results, extensive simulations have been carried out. The effectiveness of the proposed algorithm is verified by results in terms of throughput maximization under the impact of chance constraint formulation in the cognitive radio networks.