Sub-Nyquist Sampling for Reaching Theoretical Minimal Sampling Rate Boundary
This work addresses the bottleneck of sub-Nyquist sampling in blind wideband spectrum sensing by reaching the theoretical minimal rate, which is a significant improvement for cognitive radio and related applications.
The paper proposes a dual-frequency aliasing wideband converter (DAWC) that achieves perfect subband localization and waveform reconstruction at the theoretical minimum sampling rate for blind multiband spectrum sensing, outperforming state-of-the-art methods in recovery accuracy.
Wideband spectrum sensing motivates sub-Nyquist sampling architectures that exploit spectral sparsity, yet in blind scenarios where subband locations are unknown, existing schemes require sampling rates at least twice the theoretical minimum. To this end, we propose a dual-frequency aliasing wideband converter (DAWC), which partitions the multiband spectrum into non-uniform frequency intervals and selectively samples only a subset of them, requiring no prior knowledge of subband locations. We demonstrate that under mild conditions on the signal and the system, DAWC achieves perfect subband localization and waveform reconstruction at the theoretical minimum rate. Moreover, we introduce an innovative side-information-aided subspace pursuit (MSSP) algorithm exploiting the common support structure inherent in the signal column submatrices for exact recovery of the spectrum support set. Based on the restricted isometry property (RIP), we provide stable recovery guarantees for MSSP in the presence of noise. Numerical simulations show that the proposed scheme achieves superior spectrum recovery accuracy compared to state-of-the-art methods.