Wireless Personal Communications

, Volume 95, Issue 4, pp 4357–4379 | Cite as

Performance of Direct-Oversampling Correlator-Type Receivers in Chaos-Based DS-CDMA Systems Over Frequency Non-selective Fading Channels

Article
  • 61 Downloads

Abstract

In this paper, we present a study on the performance of direct-oversampling correlator-type receivers in chaos-based direct-sequence code division multiple access systems over frequency non-selective fading channels. At the input, the received signal is sampled at a sampling rate higher than the chip rate. This oversampling step is used to precisely determine the delayed-signal components from multipath fading channels, which can be combined together by a correlator for the sake of increasing the SNR at its output. The main advantage of using direct-oversampling correlator-type receivers is not only their low energy consumption due to their simple structure, but also their ability to exploit the non-selective fading characteristic of multipath channels to improve the overall system performance in scenarios with limited data speeds and low energy requirements, such as low-rate wireless personal area networks. Mathematical models in discrete-time domain for the conventional transmitting side with multiple access operation, the generalized non-selective Rayleigh fading channel, and the proposed receiver are provided and described. A rough theoretical bit-error-rate (BER) expression is first derived by means of Gaussian approximation. We then define the main component in the expression and build its probability mass function through numerical computation. The final BER estimation is carried out by integrating the rough expression over possible discrete values of the PFM. In order to validate our findings, PC simulation is performed and simulated performance is compared with the corresponding estimated one. Obtained results show that the system performance get better with the increment of the number of paths in the channel.

Keywords

Chaos-based communications Chaos-based DS-CDMA Correlator-type receiver Frequency non-selective fading channels BER performance 

Notes

Acknowledgements

This work was carried out in framework of the AREAS+ scholarship program in Erasmus Mundus Action 2 project. It was aslo partially supported by the Spanish Ministry of Economy and Competitiveness and EU FEDER under grant TEC2014-59583-C2-2-R (SUNSET project) and by the Catalan Government (ref. 2014SGR-1427).

References

  1. 1.
    Viterbi, A. J. (1995). CDMA: Principles of spread spectrum communication (1st ed.). Boston: Addison-Wesley.MATHGoogle Scholar
  2. 2.
    Peterson, R. L., Zeimer, R. E., & Borth, D. E. (1995). Introduction to spread spectrum communications. New York, NY: Prentice Hall.Google Scholar
  3. 3.
    Stavroulakis, P. (2005). Chaos applications in telecommunications. Boca Raton: CRC Press.CrossRefGoogle Scholar
  4. 4.
    Heidari-Bateni, G., & McGillem, C. D. (1992). Chaotic sequences for spread spectrum: An alternative to PN-sequences. In 1992 IEEE international conference on selected topics wireless communications, Vancouver, Canada (pp. 437–440).Google Scholar
  5. 5.
    Yu, J., & Yao, Y.-D. (2005). Detection performance of chaotic spreading LPI waveforms. IEEE Transactions on Wireless Communications, 4, 390–396.CrossRefGoogle Scholar
  6. 6.
    Heidari-Bateni, G., & McGillem, C. D. (1994). A chaotic direct-sequence spread-spectrum communication system. IEEE Transactions on Communications, 42, 1524–1527.CrossRefGoogle Scholar
  7. 7.
    Zhang, Q., & Zheng, J. (2000). Choice of chaotic spreading sequences for asynchronous DS-CDMA communication. In Proceedings of IEEE Asia-Pacific conference on CAS (pp. 642–645).Google Scholar
  8. 8.
    Cong, L., & Shaoquian, L. (2000). Chaotic spreading sequences with multiple access performance better than random sequence. IEEE Transactions on Circuits and Systems I, 47, 394–397.CrossRefGoogle Scholar
  9. 9.
    Mazzini, G., Setti, G., & Rovatti, R. (1997). Chaotic complex spreading sequences for asynchronous DS-CDMA. I. System modeling and results. IEEE Transactions on Circuits and Systems I, 44, 937–947.MathSciNetCrossRefGoogle Scholar
  10. 10.
    Rovatti, R., Setti, G., & Mazzini, G. (1998). Chaotic complex spreading sequences for asynchronous DS-CDMA. Part II. Some theoretical performance bounds. IEEE Transactions on Circuits and Systems I, 45, 496–506.MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Chen, C., Yao, K., Umeno, K., & Biglieri, E. (2001). Design of spread-spectrum sequences using chaotic dynamical systems and ergodic theory. IEEE Transactions on Circuits and Systems, 48, 1110–1114.MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Rovatti, R., Setti, G., & Mazzini, G. (1998). Toward sequence optimization for chaos-based asynchronous DS-CDMA systems. In Proceedings of the IEEE GLOBECOM, Sydney, Australia, 1998 (pp. 2174–2179).Google Scholar
  13. 13.
    Setti, G., Rovatti, R., & Mazzini, G. (1999). Synchronization mechanism and optimization of spreading sequences in chaos-based DS-CDMA systems. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E82–A, 1737–1746.Google Scholar
  14. 14.
    Jovic, B., Unsworth, C., Sandhu, G., & Berber, S. (2007). A robust sequence synchronization unit for multi-user DS-CDMA chaos-based communication systems. Signal Processing, 87, 1692–1708.CrossRefMATHGoogle Scholar
  15. 15.
    Kaddoum, G., Roviras, D., Charge, P., & Fournier-Prunaret, D. (2009). Robust synchronization for asynchronous multi-user chaos-based DS-CDMA. Signal Processing, 89, 807–818.CrossRefMATHGoogle Scholar
  16. 16.
    Vali, R., Berber, S., & Nguang, S. (2010). Effect of Rayleigh fading on noncoherent sequence synchronization for multi-user chaos based DS-CDMA. Signal Processing, 90, 1924–1939.CrossRefMATHGoogle Scholar
  17. 17.
    Azou, S., Pistre, C., Duff, L. L., & Burel, G. (2003). Sea trial results of a chaotic direct sequence spread spectrum underwater communication system. In Proceedings of the IEEE Oceans, San Diego, Calif, USA (pp. 1539–1546).Google Scholar
  18. 18.
    Tam, W. M., Lau, F. C. M., Tse, C. K., & Yip, M. M. (2002). An approach to calculating the bit-error rate of a coherent chaos shift-keying digital communication system under a noisy multiuser environment. IEEE Transactions on Circuits and Systems I, 49, 210–223.CrossRefGoogle Scholar
  19. 19.
    Vitali, S., Rovatti, R., & Setti, G. (2005). On the performance of chaos-based multicode DS-CDMA systems. Circuits, Systems, and Signal Processing, 24, 475–495.MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Lawrance, A. J., & Ohama, G. (2003). Exact calculation of bit error rates in communication systems with chaotic modulation. IEEE Transactions on Circuits and Systems I, 50, 1391–1400.MathSciNetCrossRefGoogle Scholar
  21. 21.
    Tam, W., Lau, F., Tse, C., & Lawrance, A. (2004). Exact analytical bit error rates for multiple access chaos-based communication systems. IEEE Transactions on Circuits Systems II, 51, 473–481.CrossRefGoogle Scholar
  22. 22.
    Kaddoum, G., Charge, P., Roviras, D., & Fournier-Prunaret, D. (2009). A methodology for bit error rate prediction in chaos-based communication systems. Circuits, Systems and Signal Processing, 28, 925–944.CrossRefMATHGoogle Scholar
  23. 23.
    Kaddoum, G., Charge, P., & Roviras, D. (2009). A generalized methodology for bit-error-rate prediction in correlation-based communication schemes using chaos. IEEE Communications Letters, 13, 567–569.CrossRefMATHGoogle Scholar
  24. 24.
    Rovatti, R., Mazzini, G., & Setti, G. (2001). Enhanced rake receivers for chaos-based DS-CDMA. IEEE Transactions on Circuits and Systems I, 48, 818–829.CrossRefGoogle Scholar
  25. 25.
    Mazzini, G., Rovatti, R., & Setti, G. (2001). Chaos-based asynchronous DS-CDMA systems and enhanced rake receivers: Measuring the improvements. IEEE Transactions on Circuits and Systems I, 48, 1445–1453.CrossRefGoogle Scholar
  26. 26.
    Kaddoum, G., Roviras, D., Charge, P., & Fournier-Prunaret, D. (2009). Accurate bit error rate calculation for asynchronous chaos-based DS-CDMA over multipath channel. EURASIP Journal on Advances in Signal Processing, 2009, 571307. doi:10.1155/2009/571307.CrossRefMATHGoogle Scholar
  27. 27.
    Kaddoum, G., Coulon, M., Roviras, D., & Charge, P. (2010). Theoretical performance for asynchronous multi-user chaos-based communication systems on fading channels. Elsevier Signal Processing, 90, 2923–2933.CrossRefMATHGoogle Scholar
  28. 28.
    Berber, S., & Chen, N. (2013). Physical layer design in wireless sensor networks for fading mitigation. Journal of Sensor and Actuator Networks, 2, 614–630.CrossRefGoogle Scholar
  29. 29.
    IEEE standard 802.15.4. (2011). IEEE standard for local and metropolitan area networks-part 15.4: Low-rate wireless personal area networks (LR-WPANs), (revision of IEEE Std 802.15.4-2006). New York, NY: IEEE (pp. 1–294).Google Scholar
  30. 30.
    Molisch, A. F., Balakrishnan, K., Cassioli, D., Chong, C.-C., Emami, S., Fort, A., Karedal, J., Kunisch, J., Schantz, H., Schuster, U., & Siwiak, K. (2004). IEEE 802.15.4a channel model final report. UK: The Institution of Electrical Engineers.Google Scholar
  31. 31.
    Simon, M. K., & Alouini, M. S. (2005). Fading channel characterization and modeling, in digital communication over fading channels—A unified approach to performance analysis. Boca Raton: CRC Press.Google Scholar
  32. 32.
    Gilbert, E. N. (1993). Increased information rate by oversampling. IEEE Transactions on Information Theory, 39, 1973–1976.CrossRefMATHGoogle Scholar
  33. 33.
    El-Khaldi, B., Rouvaen, J. M., Menhaj, A., & El Hillali, Y. (2006). Averaging and oversampling correlator receiver with input quantization. Digital Signal Processing, 16, 120136.CrossRefGoogle Scholar
  34. 34.
    Lee, Y. S., & Seo, B. S. (2009). OFDM receivers using oversampling with rational sampling ratios. IEEE Transactions on Consumer Electronics, 55, 1765–1770.CrossRefGoogle Scholar
  35. 35.
    Wang, C. C., et al. (2008). Zig-Bee 868/915-MHz modulator/demodulator for wireless personal area network. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 16, 936–938.CrossRefGoogle Scholar
  36. 36.
    Oh, N. J., & Lee, S. G. (2006). Building a 2.4-GHZ radio transceiver using IEEE 802.15.4. IEEE Circuits and Devices Magazine, 21, 43–51.Google Scholar
  37. 37.
    Al-Jarraha, M. A., Al-Ababnehb, N. K., Al-Ibrahimb, M. M., & Al-Jarrah, R. A. (2012). Cooperative OFDM for semi distributed detection in wireless sensor networks. AEU International Journal of Electronics and Communications, 68, 1022–1029.CrossRefGoogle Scholar
  38. 38.
    Islam, M. R., & Han, Y. S. (2011). Cooperative MIMO communication at wireless sensor network: An error correcting code approach. Journal of Sensor and Actuator Networks, 11, 9887–9903.Google Scholar
  39. 39.
    Berber, S. M. (2014). Probability of error derivatives for binary and chaos-based CDMA systems in wide-band channels. IEEE Transactions on Wireless Communications, 13, 5596–5606.CrossRefGoogle Scholar
  40. 40.
    Siddiqui, M. M. (1964). Statistical inference for Rayleigh distributions. Journal of Research of the National Bureau of Standards, 68D, 1007.MathSciNetGoogle Scholar
  41. 41.
    Grinstead, C. M., & Snell, J. L. (1997). Central limit theorem, introduction to probability (2nd ed.). Providence: American Mathematical Society.MATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Electronics and TelecommunicationsHanoi University of Science and TechnologyHanoiVietnam
  2. 2.Department of Computer ArchitectureUPC BarcelonaTechBarcelonaSpain

Personalised recommendations