Channel estimation is a challenging task, especially in high mobility applications due to the rapid variation of the propagation environment. This paper presents a new technique that exploits past channel impulse responses (CIRs) in order to trace and compensate Doppler frequency in mobile applications, enabling robust estimation of time-varying channel. Based on the fact that channel taps at different time instants can be fitted with a sinusoidal wave, a joint estimator is proposed to estimate the channel parameters. Therefore, the efficiency of the channel estimation can be improved and stringent delay requirements for the communication systems can also be satisfied. Simulation results show that system performance in terms of bit error rate (BER) is significantly improved with the proposed algorithm. 1. Introduction Channel estimation is of crucial importance for reliable coherent detection in high mobility applications, such as vehicular-to-vehicular (V2V) or vehicular-to-infrastructure (V2I) communications [1, 2]. It is complicated due to the large amount of Doppler frequency experienced by moving vehicles, and, in turn, the rate of channel variation can be sufficiently high, leading to poor channel estimation performance. Therefore, channel estimation performance in high mobility applications is generally poor. In an effort, past observations of channel impulse responses (CIRs) can be exploited to improve the channel estimation performance. However, in highly mobile environments, the direct combination of multiple observed CIRs is not effective due to the phase rotation introduced by Doppler frequency, and, hence the combination is noncoherent. Therefore, the estimation of Doppler frequency is essential to improve the channel estimation performance in mobile communication. If the total amount of Doppler frequency or change in Doppler frequency can be estimated exploiting observed past CIRs, the channel can be tracked even after the elapse of the channel coherence time, which will make communication more robust in mobile applications. One of the first studies reported in the literature for the estimation of Doppler frequency is [3], where the authors proposed estimation techniques using both complex and real envelop of the received signal. Another method to estimate the Doppler frequency in the presence of speckle and receiver noise was presented in [4]. This estimation technique is based on the correlation of the signal power spectra with an arbitrary weighting function and specifically tailored for Synthetic Aperture Radar (SAR) data. On the other
References
[1]
N. Goertz and J. Gonter, “Limits on information transmission in vehicle-to-vehicle communication,” in Proceedings of the IEEE Vehicular Technology Conference (VTC '11), pp. 1–5, Spring, 2011.
[2]
H. Trivedi, P. Veeraraghavan, S. Loke, H. P. Le, and J. Singh, “A survey of lower technologies for vehicle-to-vehicle communication,” in Proceedings of the IEEE Malaysia International Conference on Communicaitons, pp. 441–446, 2009.
[3]
P. A. Bello, “Some techniques for the instantaneous real-time measurement of multipath and Doppler spread,” IEEE Transactions on Communication Technology, vol. 3, no. 3, pp. 285–292, 1965.
[4]
R. Bamler, “Doppler frequency estimation and the Cramer-Rao bound,” IEEE Transactions on Geoscinece and Remote Sensing, vol. 29, no. 3, pp. 385–390, 1991.
[5]
T. Yücek, R. M. A. Tannious, and H. Arslan, “Doppler spread estimation for wireless OFDM systems,” in Proceedings of the IEEE/Sarnoff Symposium on Advances in Wired and Wireless Communication, pp. 233–236, April 2005.
[6]
C. Tepedelenlio？lu, A. Abdi, G. B. Giannakis, and M. Kaveh, “Estimation of Doppler spread and signal strength in mobile communications with applications to handoff and adaptive transmission,” Wireless Communications and Mobile Computing, vol. 1, no. 2, pp. 221–242, 2001.
[7]
H. Nguyen, T. Le-Ngoc, and C. C. Ko, “RLS-based joint estimation and tracking of channel response, sampling, and carrier frequency offsets for OFDM,” IEEE Transactions on Broadcasting, vol. 55, no. 2, pp. 84–94, 2009.
[8]
X. Zhang, F. Xie, W. Wang, and M. Chatterjee, “TCP throughput for vehicle-to-vehicle communications,” in Proceedings of the IEEE International Conference on Communications and Netwroking (ChinaCom '06), pp. 1–5, 2006.
[9]
M. M. Freda, J. F. Weng, and T. Le-Ngoc, “Joint channel estimation and synchronization for OFDM systems,” in Proceedings of the IEEE Vehicular Technology Conference (VTC '04), pp. 1673–1677, 2004.
[10]
C. Xiao, Y. R. Zheng, N. C. Beaulieu, et al., “Second-order statistical properties of the WSS Jakes’ fading channe simulator,” IEEE Transactions on Communications, vol. 50, no. 6, pp. 888–891, 2002.
[11]
M. J. Rahman, X. Wang, S. I. Park, and H. M. Kim, “Robust synchronization technique for mobile DTV broadcasting system,” in Proceedings of the IEEE Conference on Computer and Information Technology (ICCIT '10), pp. 216–220, 2010.
[12]
H. Minn and V. K. Bhargava, “An investigation into time-domain approach for OFDM channel estimation,” IEEE Transactions on Broadcasting, vol. 46, no. 4, pp. 240–248, 2000.
[13]
P. Handel, “Properties of the IEEE-STD-1057 four-parameter sine wave fit algorithm,” IEEE Transactions on Instrumentation and Measurement, vol. 49, no. 6, pp. 1189–1193, 2000.
[14]
E. Kreyszig, H. Kreyszig, and E. J. Norminton, Advanced Engineering Mathematics, John Wiley & Sons, 2010.
[15]
C. W. Therrien and M. Tummala, Probability for Elctrical and Computer Engineers, CRC Press, 2004.
[16]
S. Haykin, Communication Sytems, John Wiley & Sons, 2001.
[17]
International Telecommunication Union—Recommendations (ITU-R) M.1225: Guidelines for evaluation of radio transmission technologies for IMT-2000.
