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Noise power estimation is a key issue in modern wireless communicationsystems. It allows resource allocation by detecting white spectral spaceseffectively, and gives control over the communication process by adjustingtransmission power. Thus far, the proposed estimation methods in the literatureare based on spectral averaging, eigenvalues of sample covariance matrix,information theory, and statistical signal analysis. Each method ischaracterized by certain stability, accuracy and complexity. However, theexisting literature does not provide an appropriate comparison. In this paper,we evaluate the performance of the existing estimation techniques intensivelyin terms of stability and accuracy, followed by detailed complexity analysis.The basis for comparison is signal-to-noise ratio (SNR) estimation in simulatedindustrial, scientific and medical (ISM) band transmission. The source of usedbackground distortions is complex noise measurement, recorded by USRP-2932 inan industrial production area. Based on the examined solutions, we also analyzethe influence of noise samples separation techniques on the estimation process.As a response to the defects in the used methods, we propose a novel noisesamples separation algorithm based on the adaptation of rank order filtering(ROF). In addition to simple implementation, the proposed method has a verygood 0.5 dB root-mean-squared error (RMSE) and smaller than 0.1 dB resolution,thus achieving a performance that is comparable with the methods exploitinginformation theory concepts.

Quantitative Benchmarks and New Directions for Noise Power Estimation Methods in ISM Radio Environment