Photonic distributed compressive sampling of multi-node wideband sparse radio frequency signals.

A photonic distributed compressive sampling (PDCS) approach for identifying the spectra of multi-node wideband sparse signals is proposed. The scheme utilizes wavelength division multiplexing (WDM) technology to transmit multi-node signals to a central station, where distributed compressive sampling (DCS) based on the random demodulator (RD) model is employed to simultaneously identify the signal spectrum. By exploiting signal correlations among nodes, DCS achieves a higher compression ratio of the sampling rate than single-node compressive sampling (CS). In a semi-physical simulation experiment, we demonstrate the feasibility of the approach by recovering the spectra of two wideband sparse signals from nodes located 20 km and 10 km away. The spectra of two signals with a mixed support-set sparsity of 2 and 4 are recovered with a compression ratio of 8 and 4, respectively. We further investigate the impact of common parts and the number of nodes on PDCS performance through numerical simulation. The proposed system takes advantage of the ultra-high bandwidth of photonic technology and the low loss of optical fiber transmission, making it suitable for long-distance, multi-node, and large-coverage electromagnetic spectrum identification.