Energy-Based Spectrum Sensing under Nonreconstruction Framework

Abstract

To reduce the computational complexity and rest on less prior knowledge, energy-based spectrum sensing under nonreconstruction framework is studied. Compressed measurements are adopted directly to eliminate the effect of reconstruction error and high computational complexity caused by reconstruction algorithm of compressive sensing. Firstly, we summarize the conventional energy-based spectrum sensing methods. Next, the major effort is placed on obtaining the statistical characteristics of compressed measurements and its corresponding squared form, such as mean, variance, and the probability density function. And then, energy-based spectrum sensing under nonreconstruction framework is addressed and its performance is evaluated theoretically and experimentally. Simulations for the different parameters are performed to verify the performance of the presented algorithm. The theoretical analysis and simulation results reveal that the performance drops slightly less than that of conventional energy-normalization method and reconstruction-based spectrum sensing algorithm, but its computational complexity decreases remarkably, which is the first thing one should think about for practical applications. Accordingly, the presented method is reasonable and effective for fast detection in most cognitive scenarios.

Quickest Spectrum Sensing Approaches for Wideband Cognitive Radio Based On STFT and CS

“…Therefore, the CS process, including the sparse representation of matrix, the choice of observation matrix, and the reconstruction of signal, makes the sampling frequency reduce, which can be described in more detail below: Otherwise, restricted isometry property (RIP) indicates the sufficient and necessary condition that the determined solution exists [17]. If Φ adopts a random Gaussian matrix, it would obey RIP with high probability [18].…”

“…We can have the observation sequence at the cognitive user using Equation (11). The cognitive user obtains using Equation (12) after each sample, computes s using Equation (16), compares the statistics at each frequency sampling point using Equation (18), and has the detection time in each frequency band. One focus is that CS will inevitably introduce error for spectrum sensing.…”