Spectrum sensing of wide band signals based on energy detection with compressive sensing

الملخص EN

Compressive sensing (CS) technique is used to solve the problem of high sampling rate with wide band signal spectrum sensing where high speed analogue to digital converter is needed to do that.

This leads to difficult hardware implementation, large time of sensing and detection with high consumptions power.

The proposed approach combines energy-based detection, with CS compressive sensing and investigates the probability of detection, and the probability of false alarm as a function of the SNR, showing the effect of compression to spectrum sensing performance of cognitive radio system.

The Discrete Cosine Transform (DCT) is used as a sparse representation basis of the received signal, and random matrix as a compressive matrix.

The ℓ1 norm algorithm is used to reconstruct the original signal.

A closed form of probability of detection and probability of false alarm are derived.

Computer simulation shows clearly that the compression ratio, recovery error and SNR level affect the probability of detection.