Improved spectral sensing in cognitive radios using photonic-based principal component analysis

We propose and experimentally demonstrate a microwave photonic system that iteratively performs principal component analysis on partially correlated, 8-channel, 13 Gbaud signals. The system that is presented is able to adapt to oscillations in interchannel correlations and follow changing principal components. The system provides advantages in bandwidth performance and fan-in scalability that are far superior to electronic counterparts. Wideband, multidimensional techniques are relevant to >10 GHz cognitive radio systems and could bring solutions for intelligent radio communications and information sensing, including spectral sensing.