TY - GEN
T1 - Collaborative white space detection based on sample entropy and fractal theory
AU - Srinu, Sesham
AU - Mishra, Amit K.
AU - Reddy, M. Kranthi Kumar
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/4/2
Y1 - 2018/4/2
N2 - 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.
AB - 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.
KW - Cognitive radio networks
KW - collaborative detection
KW - Fractal dimension
KW - Real-time data
KW - Sample entropy
UR - https://www.scopus.com/pages/publications/85050953981
U2 - 10.1109/COMSNETS.2018.8328228
DO - 10.1109/COMSNETS.2018.8328228
M3 - Conference Proceeding (ISBN)
AN - SCOPUS:85050953981
T3 - 2018 10th International Conference on Communication Systems and Networks, COMSNETS 2018
SP - 403
EP - 406
BT - 2018 10th International Conference on Communication Systems and Networks, COMSNETS 2018
PB - Institute of Electrical and Electronics Engineers
T2 - 10th International Conference on Communication Systems and Networks, COMSNETS 2018
Y2 - 3 January 2018 through 7 January 2018
ER -