Cognitive radio has been designed for solving the problem of spectrum scarcity by using the spectrum of primary users who don’t use their spectrum on that time. For sensing the spectrum, collaborative spectrum sensing has been utilized because of robustness. In this paper, a new collaborative spectrum method is proposed based on Least Mean Square (LMS) algorithm. In this scheme, the weights of secondary users were updated in time and finally the sensing results were combined in the fusion center based on their trusted weights. Simulation results show that the proposed scheme can significantly reduce the effects of Spectrum Sensing Data Falsification (SSDF) attackers, when they are smart malicious, and even percentage of malicious users are more than trusted users.

Wireless relaying has been known to provide the improvements in link reliability, spectral efficiency, and coverage extension. In this paper, we use full duplex relays for Interference Alignment (IA) in K-users Interference Channel (IC) and show K Degrees of Freedom (DOF) is achievable. In first hop, relays receive signals from transmitters and forward them to receivers in second hop. Two iteratively algorithms are proposed for computing relays function, precoder, and decoder matrices. First algorithm minimizes leakage interference at receivers that has appropriate performance at high Signal to Noise Ratio (SNR) region. Furthermore, the second algorithm has better performance at low-mediate SNR. The performance of proposed algorithms are compared with other schemes and validated with simulation in terms of achieved sum rate.