Abstract
In this paper, a simultaneous transmission and sensing scheme based on guided independent component analysis (GICA) combined with non-Gaussian criterion is conceived for fulfilling full-duplex cognitive radio. In this proposed scheme, ICA mechanism is firstly utilized for blind separation of the observation mixtures, and then the separated signals are recognized through non-Gaussian criterion based on cumulant. In particular, the known secondary user (SU) signal is used as a signature for helping implement ICA assignment. As a key component of machine learning, ICA theory can contribute significantly to conquering the static sensing problem and residual self-interference influence existing in conventional cognitive radio systems. Simulation experiments and analysis corroborate the effectiveness of the proposed method. The performance enhancement is also demonstrated by comparing with the conventional spectrum sensing scheme.
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