Design Robust Secured Communication for Untrusted Miso Cognitive Radio Networks

Abstract

One of the great challenges of implementing spectrum sensing is the hidden terminal problem, which occurs when the cognitive radio is shadowed, in severe multipath fading or inside buildings with high penetration loss, while a primary user (PU) is operating in the vicinity. Due to the hidden terminal problem, a cognitive radio may fail to notice the presence of the PU and then will access the licensed channel and cause interference to the licensed system. To deal with the hidden terminal problem in cognitive radio networks, multiple cognitive users can cooperate to conduct spectrum sensing. It has been shown that spectrum sensing performance can be greatly improved with an increase of the number of cooperative groups. Cognitive radio allows unlicensed users to access licensed frequency bands through dynamic spectrum access to reduce spectrum scarcity. This requires intelligent spectrum sensing techniques like co-operative sensing which makes use of information from number of users. In this thesis to find the number of optimal users in a scenario to optimize the detection probability and reduce overhead leading to better utilization of resources. The multi-optimization (MOP) are using to find an optimum value of threshold. The high rate of interference and noise creates untrusted scenario of transmission. This scenario compromised with security threats of communication. For the minimization of untrusted signal used artificial neural network (ANN). The artificial neural network model work as signal filter. The design signal filter work with feedback process. The design algorithm reduces the security risk of transmission and provides reliable secured communication in cognitive radio network. This thesis investigates the use of cyclo-stationary detector and its simulation in MATLAB for licensed user detection. cyclo-stationary detector enables operation under low SNR conditions and thus saves the need for consulting more number of users. Simulation results show that implementing co-operative spectrum sensing help in