University of New Hampshire

Bessam Z Al-Jewad

Nicholas J Kirsch

Mahdi H Al-Badrawi

An Empirical Mode Decomposition (EMD)-based noise estimation process is disclosed herein that allows for blind estimations of noise power for a given signal under test. The EMD-based noise estimation process is non-parametric and adaptive to a signal, which allows the EMD-based noise estimation process to operate without necessarily having a priori knowledge about the received signal. Existing approaches to spectrum sensing such as Energy Detector (ED) and Maximum Eigenvalue Detector (MED), for example, may be modified to utilize a EMD-based noise estimation process consistent with the present disclosure to shift the same from semi-blind category to fully-blind category.

Techniques for empirical mode decomposition (EMD)-based noise estimation

Techniques for EMD-based signal de-noising are disclosed that use statistical characteristics of IMFs to identify information-carrying IMFs for the purposes of partially reconstructing the identified relevant IMFs into a de-noised signal. The present disclosure has identified that the statistical characteristics of IMFs with noise tend to follow a generalized Gaussian distribution (GGD) versus only a Gaussian or Laplace distribution. Accordingly, a framework for relevant IMF selection is disclosed that includes, in part, performing a null hypothesis test against a distribution of each IMF derived from the use of a generalized probability density function (PDF). IMFs that contribute more noise than signal may thus be identified through the null hypothesis test. Conversely, the aspects and embodiments disclosed herein enable the determination of which IMFs have a contribution of more signal than noise. Thus, a signal may be partially reconstructed based on the predominately information-carrying IMFs to result in de-noised output signal.

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Madbury, NH, United States of America

Bessam Z Al-Jewad