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The interference immunity of the telemetric information data exchange with autonomous mobile robots

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Category: Information technologies, systems analysis and administration

Last Updated on 04 June 2015

Published on 29 March 2015

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Authors:

M.I. Kozlenko, Cand. Sci. (Tech.), Associate Professor, State Higher Educational Institution “Vasyl Stefanyk Precarpathian National University”, Senior Lecturer of the IT Department, Ivano-Frankivsk, Ukraine

Abstract:

Purpose. To obtain the interference immunity of the data exchange by spread spectrum signals with variable entropy of the telemetric information data exchange with autonomous mobile robots.

Methodology. The results have been obtained by the theoretical investigations and have been confirmed by the modeling experiments.

Findings. The interference immunity in form of dependence of bit error probability on normalized signal/noise ratio of the data exchange by spread spectrum signals with variable entropy has been obtained.It has been proved that the interference immunity factor (needed normalized signal/noise ratio) is at least 2 dB better under condition of equal time complexity as compared with correlation processing methods of orthogonal signals.

Originality. For the first time the interference immunity in form of dependence of bit error probability on normalized signal/noise ratio of the data exchange by spread spectrum signals with variable entropy has been obtained.

Practical value. The obtained results prove the feasibility of using variable entropy spread spectrum signals data exchange method in the distributed telemetric information processing systems in specific circumstances. 

References:

1. Sklar, Bernard (2001), Digital Communications: Fundamentals and Applications, Prentice-Hall PTR.

2. Terrence, W. Barrett (2000), “History of ultra-wideband (UWB) radar & communications: pioneers and innovators”, Progress in Electromagnetics Symposium (PIERS 2000), Cambridge, Massachusetts, July 2000.

3. Andreyev, Yu., Dmitriev, A.S., and Starkov, S.O. (1997), “Information processing in 1-D systems with chaos”, IEEE Transactions on Circuits and Systems, vol. 44, no. 1, pp. 21–28.

4. Heiskala, J. and Terry, J. (2002), OFDM Wireless LANS: A Theoretical and Practical Guide, Sams Publishing, Indianapolis.

5. Kozlenko, M.I. (2012), “Frequency resource using of the spread spectrum signals forming in the distributed computer and telecommunication systems”, The Problems of Information Technologies, no. 1 (011), pp. 115–120.

 Козленко М.І. Ефективність використання частотної смуги при формуванні широкосмугових сигналів в розподілених комп’ютерних та телекомунікаційних системах / М.І. Козленко // Проблеми інформаційних технологій. – 2012. – № 1(011). – С. 115–120.

6. John, G. Proakis and Masoud, Salehi (2008), Digital Communications, McGraw-Hill Higher Education.

7. Kozlenko, M.I. (2012), “Time complexity of the variable entropy spread spectrum signals digital demodulation”, Visnyk Nationalnoho Tekhnichnoho Universytetu “KhPI”, Issue: Information Science and Modeling, no. 38, pp. 93–101.

 Козленко М.І. Часова складність алгоритмів цифрової демодуляції широкосмугових сигналів з керованою ентропією / М.І. Козленко // Вісник Національного технічного університету „Харківський політехнічний інститут“. Збірник наукових праць. – 2012. – № 38. –С. 93–101.

8. Mengali, U. and D’Andrea, A.N. (1997), Synchronization Technique for Digital Receivers, Plenum Press, New York.

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