Efficient compressive spectrum sensing algorithm for M2M devices

Spectrum used for Machine-to-Machine (M2M) communications should be as cheap as possible or even free in order to connect billions of devices. Recently, both UK and US regulators have conducted trails and pilots to release the UHF TV spectrum for secondary licence-exempt applications. However, it is a very challenging task to implement wideband spectrum sensing in compact and low power M2M devices as high sampling rates are very expensive and difficult to achieve. In recent years, compressive sensing (CS) technique makes fast wideband spectrum sensing possible by taking samples at sub-Nyquist sampling rates. In this paper, we propose a two-step CS based spectrum sensing algorithm. In the first step, the CS is implemented in an SU and only part of the spectrum of interest is supposed to be sensed by an SU in each sensing period to reduce the complexity in the signal recovery process. In the second step, a denoising algorithm is proposed to improve the detection performance of spectrum sensing. The proposed two-step CS based spectrum sensing is compared with the traditional scheme and the theoretical curves

New Algorithms for Wideband Spectrum Sensing Via Compressive Sensing

We consider the problem of spectrum sensing in a Cognitive Radio (CR) system when the primaries can be occupying a few subbands in a wideband spectrum. Since the primary signal dimension is large, Nyquist rate can be very high. Compressive sensing (CS) can be useful in this setup. However a CR system needs to operate at a very low SNR(similar to -20dB) where the compressive sensing techniques are usually not successful. Combining them with statistical techniques can be useful. But this has been difficult because the statistics of the parameters obtained from the recovery algorithms (e.g., OMP) are not available. We develop a suboptimal recovery algorithm COR for which the statistics can be easily approximated. This allows us to use Neyman Pearson technique as well as sequential detection techniques with CS. The resulting algorithms provide satisfactory performance at -20 dB SNR. In fact COR's recovery performance is better than OMP itself at low SNR. We also modify the algorithm for the scenario when the channel gains and the noise variance may also not be available