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.

翻译：宽带频谱感知推动了利用频谱稀疏性的亚奈奎斯特采样架构，然而在子带位置未知的盲场景中，现有方案所需采样率至少为理论最小值的两倍。为此，我们提出一种双频混叠宽带转换器（DAWC），该架构将多频带频谱划分为非均匀频率区间，并仅选择性采样其中一部分，无需子带位置的先验知识。我们证明，在信号与系统的温和条件下，DAWC能以理论最小采样率实现完美的子带定位与波形重构。此外，我们引入了一种创新性的边信息辅助子空间追踪（MSSP）算法，利用信号列子矩阵固有的公共支撑结构实现频谱支撑集的精确重构。基于受限等距性质（RIP），我们给出了MSSP在噪声环境下的稳定恢复保证。数值仿真表明，与现有最优方法相比，所提方案实现了更优的频谱恢复精度。