Optimal resource allocation for soft decision fusion-based cooperative spectrum sensing in cognitive radio networks

Less spatial-correlated secondary users (SUs) are selected based on Hungarian method.The joint optimization of sensing time and transmission power of SUs is achieved through Novel Iterative Dinkelbach method (NIDM) algorithm.The combination of parametric transformation with the Lagrangian duality provides better performance with lesser computational complexity.The proposed method is validated through the simulation results. Energy efficiency (EE) maximization with limited interference to the primary user (PU) is one of the primary concerns in cognitive radio networks (CRNs). To achieve this objective, we first propose an algorithm to select less spatially-correlated secondary users (SUs) to lessen the shadowing effect in wireless environment. Further, the aid of parametric transformation with the Lagrangian duality theorem in our proposed algorithm called Novel Iterative Dinkelbach method (NIDM) is used to optimise both sensing time and transmission power allocation of the SUs for maximising EE under the constraints of maximum transmission power, interference to the PU, overall outage of secondary transmission and minimum data rate requirement. Extensive simulation results demonstrate the effectiveness of our proposed algorithm. It is also observed that our proposed scheme outperforms the other existing schemes in enhancing the EE with the same system parameters. Display Omitted