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Computationally Efficient Wideband Spectrum Sensing through Cumulative Distribution Function and Machine Learning

Jakub Nikonowicz ; Poznan University of Technology, Poznan, Poland
Mieczysław Jessa ; Poznan University of Technology, Poznan, Poland
Łukasz Matuszewski ; Poznan University of Technology, Poznan, Poland

Puni tekst: engleski pdf 6.072 Kb

str. 38-46

preuzimanja: 42



Blind spectrum sensing (BSS) is crucial for identifying unknown signals in scenarios with limited prior knowledge.
Traditional methods face challenges with unknown and timevarying signals, especially in the presence of noise interference.
This paper addresses these issues by introducing a statistical signal processing framework that extends the use of machine learning (ML) features. Our approach improves BSS by incorporating
cumulative distribution functions (CDFs) into unsupervised ML,
enabling effective clustering of diverse transmission states without assumptions about specific noise distributions. Additionally,
we introduce a temporal decomposition technique using shorter
Fast Fourier Transforms (FFTs), enhancing the learning process,
reducing system inertia, and minimizing data requirements for
retraining under dynamic conditions. We evaluate our method,
focusing on various features/approaches for incorporating CDFs
into ML, including centroid, linear approximation, and low-order
statistics. Simulation results demonstrate robust detection in a
standard transmission scenario with a Gaussian pulse amidst
additive white Gaussian noise, maintaining a consistently low
false alarm rate. These findings highlight our BSS approach’s
effectiveness and practical potential in handling unknown signals
in challenging environments. This research provides valuable
insights, laying the groundwork for practical implementation in
real-world scenarios.

Ključne riječi

Blind detection; cumulative distribution function; machine learning; spectrum sensing; unknown signals

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