Full-Duplex Cognitive Radio Using Guided Independent Component Analysis and Cumulant Criterion

Due to the ever-increasing wireless service requirements, the issues of a limited frequency resource and underutilized frequency allocation scheme as well as frequency crowd have come to become increasingly severe. It gives rise to being difficult to effectively eliminate the spectrum interference and fulfill an efficient spectrum access mechanism using the congested frequency band. For this purpose, this paper conceives a simultaneous transmission and sensing scheme based on the guided independent component analysis combined with the cumulant-based non-Gaussian criterion for fulfilling full-duplex cognitive radio (FD-CR). The philosophy of this proposed scheme is that the ICA based blind source separation principle is firstly utilized for blind separation of the observation mixtures, and then the separated signals are recognized through correlation processing and non-Gaussian criterion based on cumulant. In particular, the known secondary user signal is used as a signature signal for assisting ICA framework construction for blind extraction of signals. As a key component of machine learning, the ICA theory can contribute significantly to conquering the static sensing problem and residual self-interference influence in existing conventional cognitive radio systems. The simulation experiments and analysis corroborate the effectiveness of the proposed method and system design. In addition, the low complexity and performance enhancement of the proposed method are also validated by comparing with that of the two practical FD-CR schemes.