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Welch FFT segment size selection method for FFT based wide band spectrum measurement

Iwata, Hiroki; Umebayashi, Kenta; Lehtomäki, Janne J.; Narieda, Shusuke (2018-07-01)

 
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URL:
https://doi.org/10.1587/transcom.2017EBP3069

Iwata, Hiroki
Umebayashi, Kenta
Lehtomäki, Janne J.
Narieda, Shusuke
Institute of Electronics
01.07.2018

Hiroki IWATA, Kenta UMEBAYASHI, Janne J. LEHTOMÄKI, Shusuke NARIEDA, Welch FFT Segment Size Selection Method for FFT Based Wide Band Spectrum Measurement, IEICE Transactions on Communications, 2018, Volume E101.B, Issue 7, Pages 1733-1743, Released July 01, 2018, [Advance publication] Released January 18, 2018, Online ISSN 1745-1345, Print ISSN 0916-8516, https://doi.org/10.1587/transcom.2017EBP3069

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© 2018 The Institute of Electronics, Information and Communication Engineers.
https://rightsstatements.org/vocab/InC/1.0/
doi:https://doi.org/10.1587/transcom.2017EBP3069
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https://urn.fi/URN:NBN:fi-fe201902155018
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Abstract

We introduce a Welch FFT segment size selection method for FFT-based wide band spectrum measurement in the context of smart spectrum access (SSA), in which statistical spectrum usage information of primary users (PUs), such as duty cycle (DC), will be exploited by secondary users (SUs). Energy detectors (EDs) based on Welch FFT can detect the presence of PU signals in a broadband environment efficiently, and DC can be estimated properly if a Welch FFT segment size is set suitably. There is a trade-off between detection performance and frequency resolution in terms of the Welch FFT segment size. The optimum segment size depends on signal-to-noise ratio (SNR) which makes practical and optimum segment size setting difficult. For this issue, we previously proposed a segment size selection method employing a relationship between noise floor (NF) estimation output and the segment size without SNR information. It can achieve accurate spectrum awareness at the expense of relatively high computational complexity since it employs exhaustive search to select a proper segment size. In this paper, we propose a segment size selection method that offers reasonable spectrum awareness performance with low computational complexity since limited search is used. Numerical evaluations show that the proposed method can match the spectrum awareness performance of the conventional method with 70% lower complexity or less.

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