RECENT ADVANCES ON SUB-NYQUIST SAMPLING-BASED WIDEBAND SPECTRUM SENSING

Cognitive radio (CR) is a promising technology enabling efficient utilization of the spectrum resource for future wireless systems. As future CR networks are envisioned to operate over a wide frequency range, advanced wideband spectrum sensing (WBSS) capable of quickly and reliably detecting idle spectrum bands across a wide frequency span is essential. In this article, we provide an overview of recent advances on sub-Nyquist sampling-based WBSS techniques, including compressed sensing-based methods and compressive covariance sensing-based methods. An elaborate discussion of the pros and cons of each approach is presented, along with some challenging issues for future research. A comparative study suggests that the compressive covariance sensing-based approach offers a more competitive solution for reliable real-time WBSS.

Automated summary

Cognitive radio technology shows promise in efficiently utilizing spectrum resources for future wireless systems, with advanced wideband spectrum sensing being crucial for operating over a wide frequency range. Recent advances in sub-Nyquist sampling-based WBSS techniques, such as compressive covariance sensing, offer competitive solutions for reliable real-time spectrum sensing.