Energy-Based Spectrum Sensing under Nonreconstruction Framework

Abstract EN

To reduce the computational complexity and rest on less prior knowledge, energy-based spectrum sensing undernonreconstruction framework is studied.

Compressed measurements are adopted directly to eliminate the effect ofreconstruction 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 onobtaining 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 nonreconstructionframework is addressed and its performance is evaluated theoretically and experimentally.

Simulations for the differentparameters are performed to verify the performance of the presented algorithm.

The theoretical analysis and simulationresults reveal that the performance drops slightly less than that of conventional energy-normalization method andreconstruction-based spectrum sensing algorithm, but its computational complexity decreases remarkably, which isthe first thing one should think about for practical applications.

Accordingly, the presented method is reasonable andeffective for fast detection in most cognitive scenarios.