Collaborative white space detection based on sample entropy and fractal theory

Srinu, Sesham and Mishra, Amit K and Reddy, M Kranthi Kumar (2018) Collaborative white space detection based on sample entropy and fractal theory. In: 10th International Conference on Communication Systems and Networks, COMSNETS, 3-7 January 2018, Bangalore, India.

Full text not available from this repository. (Request a copy)


Distinguishing deterministic signal from noise in radio spectrum to detect white spaces for cognitive radio communication is vital task. To address this, quite a few sensing algorithms have been developed based on entropy measurement. However, most of them focused only on the information content in primary user transmitted signal and ignored the hidden complexity. Hence, in this work, the techniques that quantify hidden complexity in the signal rather than only information are studied using real-time Digital Television (DTV) signals. To quantify complexity, a test statistic is developed based on linear combination of sample entropy (SaEn(LC)) at different tolerance (rt) values. Furthermore, weighted collaborative detection method based on SaEn(LC) and fractal dimension measure is proposed to improve the detection accuracy by mitigating noise encountered by single user. The results reveal that the proposed method with five nodes can detect signals up to -23dB signal-to-noise ratio.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Cognitive radio networks, collaborative detection, Fractal dimension, Real-time data, Sample entropy
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: Team Library
Date Deposited: 21 Aug 2018 04:57
Last Modified: 21 Aug 2018 04:57
Publisher URL:
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 4402 Statistics for this ePrint Item