Joint Spectrum Sensing and DOA Estimation Based on a Resource-Efficient Sub-Nyquist Array Receiver

Abstract

As the demand for wireless communication continues to surge, spectrum congestion becomes more severe. Compressive spectrum sensing with joint frequency and Direction Of Arrival (DOA) estimation is instrumental to enable efficient spectrum utilization in ultra-wideband scenarios. To reduce hardware complexity, this paper proposes a resource-efficient array undersampling structure which is distinctive in that each array element only connects to one branch of the Modulated Wideband Converter (MWC), and the modulated signals in different branches have identical periods but different waveforms. The proposed structure integrates spatial sampling by array elements and temporal undersampling by the MWC. A signal model is developed for the proposed sampling structure, which can deal with more general scenarios, involving multiple subband signals, and cross-band signals. Joint spectrum sensing algorithms are proposed based on compressed sensing and subspace decomposition. Additionally, multi-resolution grid optimization strategy is designed to eliminate grid effect with low computational complexity. We also analyze the impact of structural parameters on algorithm performance, which reveals that the number of array elements determines the maximum number of signals that can be estimated within a subband, while the equivalent total number of channels of the reception system determines the maximum number of signals that the system can estimate. Our analysis shows that the proposed sampling structure offers a greater flexibility in structural parameter selection and system design. Finally, simulations show that under the condition of the same or similar average sampling rate, the proposed structure and corresponding methods can achieve higher spectrum and DOA estimation accuracy.