Cooperative spectrum sensing in cognitive radio networks with Kernel Least Mean Square

Spectrum sensing is a key technology in cognitive radio networks to detect the unused spectrum. Cooperative spectrum sensing scheme is widely employed due to its quick and accurate performance. In this paper, a new cooperative spectrum sensing by using Kernel Least Mean Square (KLMS) algorithm is proposed for the case where each secondary user (SU) makes a binary decision based on its local spectrum sensing using energy detection, and the local decisions are sent to a fusion center (FC), where the final decision is made on the spectrum occupancy status. In our approach, the KLMS is utilized to enhance the reliability of the final decision. Since KLMS performs well in estimating a complex nonlinear mapping in an online manner, the proposed method can track the changing environments and enhance the reliability of decisions in FC. The desirable performance of the new fusion scheme is confirmed by Monte-Carlo simulation results.