A Real Time Radio Spectrum Measurement Campaign for Machine Learning Based Spectrum Inference in Cognitive Radio Network

Naikwadi, Mudassar H.; Patil, Kishor

doi:10.1007/978-981-16-3690-5_38

Cited by 2 publications

(2 citation statements)

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“…The setup for spectrum data measurement campaign is shown in Figure 1. We have used a broadband discone antenna AOR DA5000, handheld spectrum analyzer Rhode & Schwartz R&S FSH3 and a desktop PC/Laptop for data processing and analysis [22]. A low loss coaxial cable is used to connect antenna to spectrum analyzer.…”

Section: Measurement Setup and Specificationsmentioning

confidence: 99%

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A Multi-dimensional Real World Spectrum Occupancy Data Measurement and Analysis for Spectrum Inference in Cognitive Radio Network

Naikwadi¹,

Patil²

2022

Int. j. commun. netw. inf. secur.

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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.

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Section: Measurement Setup and Specificationsmentioning

confidence: 99%

“…The noise was measured for 2 hours at the same location by terminating the spectrum analyzer with 50Ω matching load. The stored data was further pre-processed and used for spectrum occupancy investigation and analysis [22] [28].…”

Section: Noise Signal Measurements and Analysismentioning

confidence: 99%