Optimization of Detection Error Rate in Cooperative Spectrum Sensing Using Multi-Objective JAYA Algorithm

Abstract

In order to make the most of the available spectrum, Cognitive Radio (CR) is used. When a licensed user isn't actively utilizing the spectrum, CR may interact with each other using it. Therefore, CR places a premium on spectrum sensing, the process of observing the presence of the main user. Cooperative spectrum sensing is a method for quickly identifying spectrum gaps in cognitive radio networks (CRNs) by merging the sensing data from different cognitive radio users. Cooperative spectrum sensing's overall performance is largely affected by how well secondary users' and the fusion center's local observations are combined. The accuracy of local observations and the data acquired by the fusion center (FC) are the two main factors that affect the detection performance. That is why system performance is affected by global decision logic and the amount of bits transferred to the fusion center (FC). However, since the sensing data collection control channel has a limited bandwidth, it is essential to quantify the received data on the energy of the signal for every user. We provide a quantized cooperative spectrum sensing method after researching coordinated spectral sensing methods based on energy detection. Through the optimization approach that is based on the Multi-Objective JAYA Algorithm, we are able to create an ideal quantized data fusion scheme that optimizes the detection error probability for a precise value of threshold. Results demonstrate that the proposed framework is valid, with the multi-objective JAYA (MOJAYA) based performance being almost identical to that of the standard soft combining technique while using less bandwidth and overhead.