PROJECT TITLE :

Optimal Hybrid Spectrum Sensing Under Control Channel Usage Constraint - 2018

ABSTRACT:

Cooperative spectrum sensing significantly improves the detection reliability of a cognitive radio network. In cooperative spectrum sensing, cognitive radio nodes send their laborious selections/detections to the fusion center via a control channel. Traditional control channels are bandwidth constrained, and a massive number of hard decisions to the fusion center might saturate the management channel, thereby degrading the performance of the network. During this Project, we have a tendency to contemplate a cooperative spectrum sensing scheme, where each cognitive radio node performs a fastened sample sensing test and sends a arduous call to the fusion center via a standard management channel. The fusion center collects the onerous choices from all cognitive radio nodes to make an observation vector, and performs a sequential likelihood ratio take a look at to form the final call. We term this sensing scheme as hybrid spectrum sensing. An optimization downside is formulated for the hybrid sensing strategy so as to maximize the cognitive radio network's throughput while considering interference (interference on primary user by cognitive radio network) and control channel's bit rate constraint. The decision thresholds for the cognitive radio nodes and the fusion center are thought-about as optimization parameters. Though the optimization downside is nonconvex, we provide an efficient algorithm for getting the global optimal resolution. In depth simulation results are provided to demonstrate the efficacy of our proposed approach.