Home // SENSORDEVICES 2023, The Fourteenth International Conference on Sensor Device Technologies and Applications // View article

Autocorrelation Average Based Sensing Technique for Cognitive Radio Networks

Authors:
Djamal Teguig
Lyes Labsis
Nacerredine Lassami

Keywords: Spectrum sensing, Cognitive radio networks, Radio Spectrum, Correlation function

Abstract:
The increasing sophistication in the technological requirements of modern life has created intractable problems in controlling and managing the limited sources of frequency bands. While all modern wireless systems mainly propose to reconsider novel methods of exploiting these frequencies. Cognitive radio network techniques required both spectrum sensing and dynamic spectrum access to solve the problem of resources management. Where, the spectrum sensing aspect provide all information about the utilization stat of frequency bands. The secondary users get actions according to this information by adopting the Dynamic Spectrum Access (DSA). Those unlicensed users get the permission to use the frequency bands of primary/licensed users when it was free. This approaches had many difficulties citing the unpredictable communication conditions first, and secondly, the amount of damage caused by any wrong sensing detection. This paper presents the detection capabilities using a novel idea based on the signal correlation proprieties. This novel technique use the average of the three first correlation lags as a statistic parameter. Starting by presenting the optimized detector parameters and its efficiency in simulation environments. Ultimately, the practical implementation serves to validate the detection capabilities of the technique within an authentic FM Radio broadcasting setting. This involves utilizing the Register Transfer-Level Software Defined Radio (RTL-SDR) dongle to capture the FM signal, while leveraging the GNU Radio software platform to both showcase the efficacy of the technique and highlight its limitations.

Pages: 1 to 6

Copyright: Copyright (c) IARIA, 2023

Publication date: September 25, 2023

Published in: conference

ISSN: 2308-3514

ISBN: 978-1-68558-091-9

Location: Porto, Portugal

Dates: from September 25, 2023 to September 29, 2023