Seda Senay



Reduced Length Chirp Pilots for Estimation of Linear Time-Varying Communication Channels

pdf PDF


In wireless communication multipath fading causes significant performance degradation and necessitates channel estimation. Transmission of two consecutive chirps with different rates as a pilot sequence is a method that has been used in the estimation of linear time-varying (LTV) channel parameters. In this paper, we propose an improvement on the chirp based channel estimation method for LTV model. We show that combination of a chirp with its complex conjugate, in particular a frequency modulated sinusoid, provides us an efficient pilot sequence. Besides reducing the length of the pilot sequence by half, the length and the rate of our proposed pilot sequence can be adjusted to comply with a-priori information on the channel. We implement the proposed method for an orthogonal frequency division multiplexing (OFDM) communication system and compare with conventional two chirps method.


Chirp pilot; time-varying channel; time-frequency methods; OFDM


[1] Takaoka, S., and Adachi, F., “Pilot-assisted adaptive interpolation channel estimation for OFDM signal reception,”in Proc of Vehicular Technology Conference, vol.3, pp. 1777-1781, May 2004.

[2] Byun, J. and Natarajan, N. P.,“Adaptive pilot utilization for OFDM channel estimation in a time varying channel,”in Proc of Wireless and Microwave Technology Conference, Clearwater, FL, pp. 1-5, April 2009.

[3] Ozaki, K., Tomitsuka, K., Okazaki, A., Sano, H., and Kubo, H., “Channel estimation technique for OFDM systems spread by chirp sequences,” IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC), Sydney, NSW, pp. 2125- 2130, 2012.

[4] Barbarossa, S. and Scaglione, A.,“Time-varying fading channels.” Chapter 4 of Signal Processing Advances in Communications, G. Giannakis, H. Hua, P. Stoica and L. Tong (Eds.), Prentice Hall, 2001.

[5] Senay, S., Akan, A., and Chaparro, L. F., “Time-frequency channel modeling and estimation of multicarrier spread spectrum communication systems,” Journal of Franklin Institute, vol. 342, pp. 776–792, Nov. 2005.

[6] Hao, H., Wang, H., Wanghan, L. V., Chen, L., “A Super-Resolution channel estimation algorithm using convex programming,”IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, Vol. E100.A, No. 5 pp. 1236–1239, 2017.

[7] Lin, Z., Guang, Y., Ming, L., Lanjun, L., and Jianguo, W., “A high accuracy multi-parameter estimation algorithm for underwater acoustic channels,” IEEE 12th International Conference on Communication Technology, Nanjing, pp. 921–924, 2010.

[8] Ziomek, L. J., Underwater Acoustics: A Linear Systems Theory Approach, Florida Academic Press, 1985.

[9] Cook, C. E. and Bernfeld, M. R., Radar Signals: An Introduction to theory and Application Academic Press,1967.

[10] Winkley, M. R., “Chirp signals for communications,” IEEE WESCON, 1962.

[11] Guang, Y., Wei, J., Yin, Ming, L., and Ling, H. Z., “An effective Sine-Chirp signal for multiparameter estimation of underwater acoustic channel,” The Journal of the Acoustical Society of America, vol. 135, no. 4, 2014.

[12] Barbarossa, S. and Swami, A., “Estimation of time-varying multipath channel parameters using chirp signals,” ISIT2001, Washington, DC, June 24-29, 2001.

[13] Shen, H., Machineni, S., and Papandreou- Suppapola, A., “Time-varying multichirp rate modulation for multiple access systems,” IEEE Trans. Signal Proc., vol.11, pp. 497–500, May 2004.

[14] Van Nee, R., and Prasad, R., OFDM for Wireless Communications. Artek House Publishers, 2000.

[15] Xia, X. G., “System identification using chirp signals and time-variant filters in the joint timefrequency domain,” IEEE Trans. Signal Processing, vol. 45, pp. 2072-2084, Aug. 1997.

[16] Bultan, A., Haddad, R. A.,“Channel estimation in noisy conditions using time-frequency domain filtering,” Signals, Systems and Computers, ASILOMAR, vol. 2, pp. 1642-1646, 24-27, Oct. 1999.

[17] Matz, G., and Hlawatsch, F., “Time-varying communication channels: Fundamentals, recent developments, and open problems,” 14th European IEEE Signal Processing Conference, 2006.

[18] Giannakis, G., and Tepedelenlioglu, B., “Basis expansion models and diversity techniques for blind identification and equalization of timevarying channels,” Proc. of the IEEE, Vol 86, pp. 1969-1986, Oct. 1998.

[19] Grenwood, D. J., and Hanzo, L., “Characterization of mobile radio channels,” in Mobile Radio Communications, ed. by R. Steele, IEEE Press, Piscataway, NJ, 1992.

[20] Tan, J and Stuber, G., “Anti-jamming performance of multi-carrier spread spectrum with constant envelope,” IEEE Intl. Conf. on Comm., vol. 1, pp. 743–747, May, 2001.

[21] Claasen, T., and Mecklenbrauker, W., “The Wigner distribution —a tool for time-frequency signal analysis, ” Philips J. Res., vol. 35, No. 3, 4/5, 6, 217-250, 276-300, 372-389, 1980.

[22] Stoica, P., and Moses, R., “Spectral Analysis of Signals, ” Pearson, Prentice-Hall, 2005.

Cite this paper

Seda Senay. (2017) Reduced Length Chirp Pilots for Estimation of Linear Time-Varying Communication Channels. International Journal of Communications, 2, 154-159


Copyright © 2017 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0