Joint multiband signal detection and cyclic spectrum estimation from compressive samples

This paper focuses on wide-sense stationary signal processing within a compressive sensing framework, proposing a new method of compressive sampling fast Fourier transform (FFT) accumulation method (CS-FAM). Depending on how it is applied, CS-FAM has one or two steps, allowing for versatility in multiband signal detection and parameter extraction. In the first step, the active sub-bands are detected using multiple measurement vectors (MMVs) and multiuser detection is achieved using a bandwidth constraint. In applications where it is required, such as in estimations of carrier frequency, symbol rate, or modulation format identification, the second step can be used to reconstruct the cyclic spectrums of each user individually. Based on the results of first step, parameter extraction is performed by searching for peaks in the cyclic spectrum rather than by the usual method of setting a threshold. Compared to other cyclic feature detection methods based on sub-Nyquist sampling, CS-FAM is low in complexity, allowing for practical implementation. Based on the results of the first step, parameter extraction from the cyclic spectrum is performed by searching for peaks rather than by setting a threshold. Although CS-FAM can only be employed for multiband signal detection, compared to other cyclic feature detection methods based on sub-Nyquist sampling, it is low in complexity, which makes practical implementation possible. Numerical simulations are presented to demonstrate the robustness of CS-FAM's multiband signal detection and the effectiveness of its cyclic spectrum estimation against both sampling rate reduction and noise uncertainty.