Random number generator based on RF spectrum sensing: energy detector and spectral correlation function approach

Ali Rıza Ekti

Öz


Wireless communication is open and more vulnerable against to authorized and unauthorized users due to the broadcasting nature of wireless radio propagation in contrast to wired networks where two devices connected to each other physically through cables. Thus, assuring a secure wireless radio communication is an important and mandatory task for the 5G and beyond wireless networks. In order to prevent the manipulation and to ensure the privacy of the information, secure cryptographic algorithms are necessary. The performance of the cryptographic algorithms heavily relies on the generation of random keys which are created from the seeds and these seeds must be random. By utilization of the random nature of the wireless spectrum, secure random keys can be produced. Therefore, in this study, spectrum sensing based random number generator (RNG) is proposed in order to detect the unknown received signal and extract the noise part of the signal by simply adopting the second order statistics of the cyclostationary process, spectral correlation function and the energy detector approaches. However, utilization of probability mass function output statistics is also introduced to distinguish the noise and unknown signal. A measurement setup is developed also considering line of sight conditions. Obtained noise statistics are used to generate the random bit streams and the results are fed into the NIST 800-22 test suite to show how well the performance of the spectrum sensing based random number generator. High quality random numbers are obtained which implies that spectrum sensing based RNG can provide secure data transfer directly without any other physical device.

Tam Metin:

PDF

Referanslar


Andrews, J.G., Seven ways that HetNets are a cellular paradigm shift, IEEE Communications Magazine, 51, 136–144, (2013).

Bennis, M., Simsek, M., Czylwik, A., Saad, W.; Valentin, S., Debbah, M., When cellular meets WiFi in wireless small cell networks, IEEE Communications Magazine, 51, 44–50, (2013).

Ericsson, M.R., Realizing smart manufacturing through, IOT, (2018).

Bagini, V., Bucci, M., A design of reliable true random number generator for cryptographic applications. International Workshop on Cryptographic Hardware and Embedded Systems. Springer, 204–218, (1999).

Schneier, B., Applied cryptography: protocols, algorithms, and source code in C; John Wiley & Sons, (2007).

Hong, S.L., Liu, C., Sensor-based random number generator seeding, IEEE Access, 3, 562–568, (2015).

Demir, K., Ergün, S., An analysis of deterministic chaos as an entropy source for random number generators, Entropy, 20, 957, (2018).

Lee, K., Lee, S.Y., Seo, C., Yim, K., TRNG (True Random Number Generator) method using visible spectrum for secure communication on 5G network, IEEE Access, 6, 12838–12847, (2018).

Dobre, O.A., Signal identification for emerging intelligent radios: Classical problems and new challenges, IEEE Instrumentation & Measurement Magazine, 18, 11–18, (2015).

Cabric, D., Tkachenko, A., Brodersen, R., Experimental study of spectrum sensing based on energy detection and network cooperation. Proceedings of the first international workshop on Technology and policy for accessing spectrum. ACM, p. 12, (2006).

Yarkan, S., Halbawi, W., Qaraqe, K.A., An experimental setup for performance evaluation of spectrum sensing via energy detector: indoor environment. Proceedings of the 4th International Conference on Cognitive Radio and Advanced Spectrum Management. ACM, p. 42, (2011).

Yarkan, S., A Generic Measurement Setup for Implementation and Performance Evaluation of Spectrum Sensing Techniques: Indoor Environments, IEEE Trans. Instr. and Meas., 64, 606–614, (2015).

Yucek, T., Arslan, H., A survey of spectrum sensing algorithms for cognitive radio applications, Communications Surveys & Tutorials, IEEE, 11, 116–130, (2009).

Pawelczak, P., Nolan, K., Doyle, L., Oh, S.W., Cabric, D,. Cognitive radio: Ten years of experimentation and development, IEEE Communications Magazine, 49, 90–100, (2011).

Dillard, R.A., Detectability of Spread–Spectrum Signals, IEEE Transactions on Aerospace and Electronic Systems, AES – 15, 526 – 537, (1979).

Gardner,W.A., Exploitation of spectral redundancy in cyclostationary signals, IEEE Signal Process. Mag., 8, 14–36, (1991).

Roberts, R.S., Brown, W.A., Loomis, H.H., Computationally efficient algorithms for cyclic spectral analysis, IEEE Signal Process. Mag., 8, 38–49, (1991).

Karami, E., Dobre, O.A., Adnani, N., Identification of GSM and LTE signals using their second-order cyclostationary, IEEE Intl. Instrum. and Meas. Tech. Conf. (I2MTC). IEEE, 1108–1112, (2015).

Bassham, L.E., Rukhin, A.L., Soto, J., Nechvatal, J.R., Smid, M.E., Leigh, S.D., Levenson, M., Vangel, M., Heckert, N.A., Banks, D.L., A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications – NIST. Technical Report, (2010).

Proakis, J.G., Digital Communications; McGraw–Hill, New York, 2001.

Giannakis, G.B., Cyclostationary signal analysis, Digital Signal Processing Handbook, 17–1, (1998).


Refback'ler

  • Şu halde refbacks yoktur.


Telif Hakkı (c) 2020 Ali Rıza Ekti

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.