Author(s):
1. Mudassar H. Naikwadi:
Research Scholar, 2Professor, Department of E&TC, Sinhgad Academy of Engineering, Kondhwa (Bk), Pune, Maharashtra, India
2. Kishor P. Patil:
Research Scholar, 2Professor, Department of E&TC, Sinhgad Academy of Engineering, Kondhwa (Bk), Pune, Maharashtra, India
Abstract:
Spectrum Inference in contrast to Spectrum Sensing is an active technique for dynamically inferring radio spectrum state in Cognitive Radio Networks. Efficient spectrum inference demands real world multi-dimensional spectral data with distinct features. Spectrum bands exhibit varying noise floors; an effective band wise noise thresholding guarantees an accurate occupancy data. In this work, we have done an extensive real world spectrum occupancy data measurement in frequency range 0.7 GHz to 3 GHz for tele density wise varying locations at Pune, Solapur and Kalaburagi with time diversity ranging from 2 to 7 days. We have applied maximum noise (Max Noise), m-dB and probability of false alarm (PFA) noise thresholding for spectrum occupancy calculations in all bands and across all locations. Overall occupancy across these locations is 37.89 %, 18.90 % and 13.69 % respectively. We have studied signal to noise ratio (SNR), channel vacancy length durations (CVLD) and service congestion rates (SCR) as characteristic features of measured multi-dimensional spectrum data. The results reveal strong time, spectral and spatial correlations of these features across all locations. These features can be used for a multi-dimensional spectrum inference in cognitive radio based on machine learning.
Page(s):
244-260
DOI:
DOI not available
Published:
Journal: International Journal of Communication Networks and Information Security, Volume: 14, Issue: 2, Year: 2022
Keywords:
machine learning
,
Cognitive Radio Network
,
spectrum measurement
,
Spectrum sensing
,
spectrum inference
References:
References are not available for this document.
Citations
Citations are not available for this document.