Feature-based compressive signal processing (CSP) measurement design for the pattern analysis of Cognitive Radio spectrum

Cognitive Radio (CR) can efficiently utilize the licensed wideband spectrum whenever the primary users (PUs) are absent. Spectrum sensing is the first step and an important function to fulfill the CR system. A cyclostationary feature detector can robustly detect the PU's modulated signals even under strong interferences. However this detector needs high signal sampling rate and also puts heavy computation burden on the system. Compressive sensing (CS) can compress the data at the front sampling end but has high overload and delay from the reconstruction side. In this work we generate the compressive CR spectrum measurement by utilizing both the cyclostationary feature and sparsity prior knowledge at the spectrum sensing front end, and we apply the compressive signal processing (CSP) without the need of signal or feature reconstruction. This can significantly shorten the CR spectrum sensing time. Our experimental results have shown the pattern analysis accuracy and efficiency of our CSP scheme.