An Enhanced Photonic-Assisted Sampling Approach for Spectrum-Sparse Signal by Compressed Sensing

Abstract

Spectrum-sparse signals are vital for wideband radar and wireless communication applications. A high-speed analog-to-digital converter (ADC) with the capacities of tens of gigahertz sampling rates is often required to acquire these signals. In this work, an enhanced photonic-assisted sampling approach with the combination of the photonic-assisted time-interleaved ADC and compressed sensing techniques is presented, which enables the measured signal to be reconstructed through very few samples by utilizing the sparsity of the spectrum-sparse signal. An ultrahigh spectral resolution Fourier dictionary was introduced to suppress the spectrum leakage and obtain the actual sparse expression of the spectrum-sparse signals. Moreover, a layered tracking orthogonal matching pursuit signal recovery algorithm was employed to reduce computational complexity and enhance processing speed. The performance of the proposed approach has been investigated via simulations and laboratory experiments by varying the applied spectrum-sparse signals over 100 times. The experimental results demonstrate that the proposed method can capture the blind-frequency spectrum-sparse signal at an equivalent sampling rate of 1 GS/s by utilizing four parallel ADCs with a sampling rate of 50 MS/s. It is proven that the proposed approach achieves $\sim 5$ times higher equivalent sampling rate than that of the conventional PTIADC at the same sampling rate. This work provides a valuable method for acquiring spectrum sparse signals in practical applications.

Keywords

• Photonics-assisted ADC
• compressed sensing
• time-interleaved
• signal recovery algorithm