Soft fusion-based cooperative spectrum sensing using particle swarm optimization for cognitive radio networks in cyber-physical systems

As a multi-dimensional complex system, Cyber physical systems (CPS) integrates computing, network, and physical environment. How to effectively fuse the local detection results is the key to improve the sensing performance in CPS. In order to improve the spectrum sensing performance of cognitive radio, a soft fusion-based cooperative spectrum sensing using particle swarm optimization (PSO) for cognitive radio networks is proposed. To find the optimal weighting coefficient of soft fusion, the traditional PSO algorithm is investigated and improved. To further reduce the local convergence of PSO and accelerate the convergence speed of the algorithm, the immune algorithm is introduced and chaotic sequence mechanism is applied to ensure the diversity of the particles. Moreover, the inertia weight is adjusted adaptively according to the convergence of particles so as to maintain the tradeoff between the search ability and the convergence of particles. Simulation results show that the proposed algorithm can achieve fast convergence in CPS. Compared with other typical methods, it can obtain better sensing performance under different signal-to-noise ratio environments and the constraint of false alarm probability.