Information-Theoretic Criteria for Bluetooth Signal Detection

Authors

  • Saleh A. AJOUAT Libyan Center for Engineering Research and Information Technology L.C.E.R.I.T Bani Walid/ Libya
  • Ismail S. Almarimi Department of Communication Engineering, College of Electronic Technology, Bani Walid, Libya

DOI:

https://doi.org/10.61952/jlabw.v2i1.480

Keywords:

Minimum Description Length, MDL Onset, Transient Detection, RF Signal

Abstract

Detecting the onset point of Bluetooth signals is a foundational step in wireless communication analysis and signal processing applications. This study introduces an information-theoretic approach named as Minimum Description Length (MDL) principle as a technique for identifying signal transition point based on statistical complexity. MDL works by splitting the signal into chunks and picking the point where the model description is most tight, as a result getting rid of redundancy. The MDL affords a flexible and decision-making process to detect transformation without making threshold. The technique is introduced in a comparative way with Akaike Information Criterion AIC and examined using real Bluetooth signal dataset, showing its ability to recognize the true signal effectively. The suggested method uses the embedded statistical features of the signal, offering a clear system for device weak-up moment recognition in RF signal processing.

References

Ali, Aysha & Kara, Ali & Uzundurukan, Emre. (2022). Assessment of Features and Classifiers for Bluetooth RF Fingerprinting. IEEE Access. 7. 50524-50535.

1109/ACCESS.2019.291145.

AJOUAT S. A., TEZEL N. S., “Radio frequency transient segment detection based on Akaike Information Criterion”, Politeknik Dergisi, 25(4): 1681-1686, (2022). https://doi.org/10.2339/politeknik.967341

Rissanen, J. (1989). Stochastic Complexity in Statistical Inquiry.

Y. Yuan, Z. Huang, H. Wu, and X. Wang, “Specific emitter identification based on Hilbert-Huang transform-based timefrequency-energy distribution features,” IET Communications, vol. 8, no. 13, pp. 2404–2412, 2014.

F. Salahdine, H. El Ghazi, N. Kaabouch, and W. F. Fihri, "Matched filter detection with dynamic threshold for cognitive radio networks," in Proc. IEEE Int. Conf. Wireless Networks and Mobile Communications (WINCOM), 2015.

Uzundurukan, Emre & Ali, Aysha & Dalveren, Yaser & Kara, Ali. “Performance Analysis of Modular RF Front End for RF Fingerprinting of Bluetooth Devices”, (2020). Wireless Personal Communications. 112. 10.1007/s11277-020-07162-z.

Adesina, D., Hsieh, C. C., Sagduyu, Y. E., & Qian, L. (2022). Adversarial Machine Learning in Wireless Communications: Challenges and Opportunities. Wireless Communications and Mobile Computing, 2022, 1-14.

Z. Bao, Y. Lin, S. Zhang, Z. Li and S. Mao, "Threat of Adversarial Attacks on DL-Based IoT Device Identification," in IEEE Internet of Things Journal, vol. 9, no. 11, pp. 9012-9024, 1 June1, 2022, doi: 10.1109/JIOT.2021.3120197.

Grünwald, Peter. “The Minimum Description Length Principle”, (2007). 10.7551/mitpress/4643.001.0001.

Davis, Richard & Lee, Thomas & Rodriguez-Yam, Gabriel. (2006). Structural Break Estimation for Nonstationary Time Series Models. Journal of the American Statistical Association. 101. 223-239. 10.1198/016214505000000745.

S. Evans, S. Markham, A. Torres, A. Kourtidis and D. Conklin, "An Improved Minimum Description Length Learning Algorithm for Nucleotide Sequence Analysis," 2006 Fortieth Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, 2006, pp. 1843-1850, doi: 10.1109/ACSSC.2006.355081.

Hinton, G. E., & van Camp, D. Keeping the neural networks simple by minimizing the description length of the weights, (1993). Proceedings of the 6th Annual Conference on Computational Learning Theory (COLT '93).

O. Ureten and N. Serinken, “Wireless security through RF fingerprinting,” in Canadian Journal of Electrical and Computer Engineering, Winter 2007. https://doi.org/10.1109/CJECE.2007.364330

Hall, Jeyanthi et al. “Detecting rogue devices in bluetooth networks using radio frequency fingerprinting.” CCN (2006).

Uzundurukan, E. & Dalveren, Y. & Kara, A. “A Database for the Radio Frequency Fingerprinting of Bluetooth Devices”, (2020). https://doi.org/10.3390/data5020055

N. Maeda, “A method for reading and checking phase time in auto-processing system of seismic wave data,” Zisin (Journal of the Seismological Society of Japan. 2nd ser.), vol. 38, (1985).

Wax, Mati & Kailath, Thomas “Detection of signals by information theoretic criteria. Acoustics”, (1985)., Speech and Signal Processing, IEEE Transactions on. 33. 387 - 392. 10.1109/TASSP.1985.1164557.

Downloads

Published

2026-03-04

How to Cite

Saleh A. AJOUAT, & Ismail S. Almarimi. (2026). Information-Theoretic Criteria for Bluetooth Signal Detection. Journal of Libyan Academy Bani Walid, 2(1), 368–377. https://doi.org/10.61952/jlabw.v2i1.480

Issue

Section

Applied Sciences