Low Complexity Time Synchronization Algorithm for OFDM Systems with Repetitive Preambles

In this paper, a new time synchronization algorithm for OFDM systems with repetitive preamble is proposed. This algorithm makes use of coarse and fine time estimation; the fine time estimation is performed using a cross-correlation similar to previous proposals in the literature, whereas the coarse time estimation is made using a new metric and an iterative search of the last sample of the repetitive preamble. A complete analysis of the new metric is included, as well as a wide performance comparison, for multipath channel and carrier frequency offset, with the main time synchronization algorithms found in the literature. Finally, the complexity of the VLSI implementation of this proposal is discussed. © 2011 Springer Science+Business Media, LLC.This work was supported by the Spanish Ministerio de Educacion y Ciencia under grants TEC2006-14204-C02-01 and TEC2008-06787.Canet Subiela, MJ.; Almenar Terre, V.; Flores Asenjo, SJ.; Valls Coquillat, J. (2012). Low Complexity Time Synchronization Algorithm for OFDM Systems with Repetitive Preambles. Journal of Signal Processing Systems. 68(3):287-301. doi:10.1007/s11265-011-0618-6S287301683IEEE 802.11a standard (1999). Wireless LAN medium access control (MAC) and physical layer (PHY) specifications: high-speed physical layer in the 5 GHz band.IEEE 802.11 g standard (2003). Wireless LAN specifications: Further higher data rate extension in the 2.4 GHz band.IEEE 802.16-2004 (2004). Standard for local and metropolitan area networks, part 16: Air interface for fixed broadband wireless access systems.Lee, D., & Cheun, K. (2002). Coarse symbol synchronization algorithms for OFDM systems in multipath channels. IEEE Communications Letters, 6(10), 446–448.Park, B., Cheon, H., Ko, E., Kang, C., & Hong, D. (2004). A blind OFDM synchronization algorithm based on cyclic correlation. IEEE Signal Processing Letters, 11(2), 83–85.Beek, J. J., Sandell, M., & Börjesson, P. O. (1997). ML estimation of time and frequency offset in OFDM system. IEEE Transactions on Signal Processing, 45(7), 1800–1805.Ma, S., Pan, X., Yang, G., & Ng, T. (2009). Blind symbol synchronization based on cyclic prefix for OFDM systems. IEEE Transactions on Vehicular Technology, 58(4), 1746–1751.Schmidl, T., & Cox, D. (1997). Robust frequency and timing synchronization for OFDM. IEEE Transactions on Communications, 45(12), 1613–1621.Coulson, A. J. (2001). Maximum likelihood synchronization for OFDM using a pilot symbol: Algorithms. IEEE Journal on Selected Areas in Communications, 19(12), 2495–2503.Tufvesson, F., Edfors, O., & Faulker, M. (1999). Time and frequency synchronization for OFDM using PN-sequence preambles. Proceedings of the Vehicular Technology Conference (VTC), 4, 2203–2207.Shi, K., & Serpedin, E. (2004). Coarse frame and carrier synchronization of OFDM systems: a new metric and comparison. IEEE Transactions on Wireless Communications, 3(4), 1271–1284.Minn, H., Zeng, M., & Bhargava, V. K. (2000). On timing offset estimation for OFDM Systems. IEEE Communications Letters, 4, 242–244.Minn, H., Bhargava, V. K., & Letaief, K. B. (2003). A robust timing and frequency synchronization for OFDM systems. IEEE Transactions on Wireless Communications, 2(4), 822–839.Minn, H., Bhargava, V. K., & Letaief, K. B. (2006). A combined timing and frequency synchronization and channel estimation for OFDM. IEEE Transactions on Communications, 54(3), 416–422.Park, B., Cheon, H., Ko, E., Kang, C., & Hong, D. (2003). A novel timing estimation method for OFDM systems. IEEE Communications Letters, 7(5), 239–241.Chang, S., & Kelley, B. (2003). Time synchronization for OFDM-based WLAN systems. Electronics Letters, 39(13), 1024–1026.Wu, Y., Yip, K., Ng, T., & Serpedin, E. (2005). Maximum-likelihood symbol synchronization for IEEE 802.11a WLANs in unknown frequency-selective fading channels. IEEE Transactions on Wireless Communications, 4(6), 2751–2763.Larsson, E. G., Liu, G., Li, J., & Giannakis, G. B. (2001). Joint symbol timing and channel estimation for OFDM based WLANs. IEEE Communications Letters, 5(8), 325–327.Troya, A., Maharatna, K., Krstic, M., Grass, E., Jagdhold, U., & Kraemer, R. (2007). Efficient inner receiver design for OFDM-based WLAN systems: algorithm and architecture. IEEE Transactions on Wireless Communications, 6(4), 1374–1385.Yang, J., & Cheun, K. (2006). Improved symbol timing synchronization in IEEE 802.11a/g wireless LAN systems in multipath channels. International Conference on Consumer Electronics. doi: 10.1109/ICCE.2006.1598425 .Manusani, S. K., Hshetrimayum, R. S., & Bhattacharjee, R. (2006). Robust time and frequency synchronization in OFDM based 802.11a WLAN systems. Annual India Conference. doi: 10.1109/INDCON.2006.302775 .Zhou, L., & Saito, M. (2004). A new symbol timing synchronization for OFDM based WLANs under multipath fading channels. 15th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications. doi: 10.1109/PIMRC.2004.1373890 .Kim, T., & Park, S.-C. (2007). A new symbol timing and frequency synchronization design for OFDM-based WLAN systems. 9th Conference on Advanced Communication Technology. doi: 10.1109/ICACT.2007.358691 .Baek, J. H., Kim, S. D., & Sunwoo, M. H. (2008). SPOCS: Application specific signal processor for OFDM communication systems. Journal of Signal Processing Systems, 53(3), 383–397.Van Kempen, G., & van Vliet, L. (2000). Mean and variance of ratio estimators used in fluorescence ratio imaging. Cytometry, 39(4), 300–305.J. Melbo, J., & Schramm, P. (1998). Channel models for HIPERLAN/2 in different indoor scenarios. 3ERI085B, HIPERLAN/2 ETSI/BRAN contribution.Abramowitz, M., & Stegun, I. A. (1972). Handbook of mathematical functions. Dover.López-Martínez, F. J., del Castillo-Sánchez, E., Entrambasaguas, J. T., & Martos-Naya, E. (2010). Iterative-gradient based complex divider FPGA core with dynamic configurability of accuracy and throughput. Journal of Signal Processing Systems. doi: 10.1007/s11265-010-0464-y .Angarita, F., Canet, M. J., Sansaloni, T., Perez-Pascual, A., & Valls, J. (2008). Efficient mapping of CORDIC Algorithm for OFDM-based WLAN. Journal of Signal Processing Systems, 52(2), 181–191

Timing and Frequency Synchronization and Channel Estimation in OFDM-based Systems

Orthogonal frequency division multiplexing (OFDM) due to its appealing features, such as robustness against frequency selective fading and simple channel equalization, is adopted in communications systems such as WLAN, WiMAX and DVB. However, OFDM systems are sensitive to synchronization errors caused by timing and frequency offsets. Besides, the OFDM receiver has to perform channel estimation for coherent detection. The goal of this thesis is to investigate new methods for timing and frequency synchronization and channel estimation in OFDM-based systems. First, we investigate new methods for preamble-aided coarse timing estimation in OFDM systems. Two novel timing metrics using high order statistics-based correlation and differential normalization functions are proposed. The performance of the new timing metrics is evaluated using different criteria including class-separability, robustness to the carrier frequency offset, and computational complexity. It is shown that the new timing metrics can considerably increase the class-separability due to their more distinct values at correct and wrong timing instants, and thus give a significantly better detection performance than the existing timing metrics do. Furthermore, a new method for coarse estimation of the start of the frame is proposed, which remarkably reduces the probability of inter-symbol interference (ISI). The improved performances of the new schemes in multipath fading channels are shown by the probabilities of false alarm, missed-detection and ISI obtained through computer simulations. Second, a novel pilot-aided algorithm is proposed for the detection of integer frequency offset (IFO) in OFDM systems. By transforming the IFO into two new integer parameters, the proposed method can largely reduce the number of trial values for the true IFO. The two new integer parameters are detected using two different pilot sequences, a periodic pilot sequence and an aperiodic pilot sequence. It is shown that the new scheme can significantly reduce the computational complexity while achieving almost the same performance as the previous methods do. Third, we propose a method for joint timing and frequency synchronization and channel estimation for OFDM systems that operate in doubly selective channels. Basis expansion modeling (BEM) that captures the time variations of the channel is used to reduce the number of unknown channel parameters. The BEM coefficients along with the timing and frequency offsets are estimated by using a maximum likelihood (ML) approach. An efficient algorithm is then proposed for reducing the computational complexity of the joint estimation. The complexity of the new method is assessed in terms of the number of multiplications. The mean square estimation error of the proposed method is evaluated in comparison with previous methods, indicating a remarkable performance improvement by the new method. Fourth, we present a new scheme for joint estimation of CFO and doubly selective channel in orthogonal frequency division multiplexing systems. In the proposed preamble-aided method, the time-varying channel is represented using BEM. CFO and BEM coefficients are estimated using the principles of particle and Kalman filtering. The performance of the new method in multipath time-varying channels is investigated in comparison with previous schemes. The simulation results indicate a remarkable performance improvement in terms of the mean square errors of CFO and channel estimates. Fifth, a novel algorithm is proposed for timing and frequency synchronization and channel estimation in the uplink of orthogonal frequency division multiple access (OFDMA) systems by considering high-mobility situations and the generalized subcarrier assignment. By using BEM to represent a doubly selective channel, a maximum likelihood (ML) approach is proposed to jointly estimate the timing and frequency offsets of different users as well as the BEM coefficients of the time-varying channels. A space-alternating generalized expectation-maximization algorithm is then employed to transform the maximization problem for all users into several simpler maximization problems for each user. The computational complexity of the new timing and frequency offset estimator is analyzed and its performance in comparison with that of existing methods using the mean square error is evaluated . Finally, two novel approaches for joint CFO and doubly selective channel estimation in the uplink of multiple-input multiple-output orthogonal frequency division multiple access (MIMO-OFDMA) systems are presented. Considering high-mobility situations, where channels change within an OFDMA symbol interval, and the time varying nature of CFOs, BEM is employed to represent the time variations of the channel. Two new approaches are then proposed based on Schmidt Kalman filtering (SKF). The first approach utilizes Schmidt extended Kalman filtering for each user to estimate the CFO and BEM coefficients. The second approach uses Gaussian particle filter along with SKF to estimate the CFO and BEM coefficients of each user. The Bayesian Cramer Rao bound is derived, and performance of the new schemes are evaluated using mean square error. It is demonstrated that the new schemes can significantly improve the mean square error performance in comparison with that of the existing methods

Channel, Phase Noise, and Frequency Offset in OFDM Systems: Joint Estimation, Data Detection, and Hybrid Cramer-Rao Lower Bound

Oscillator phase noise (PHN) and carrier frequency offset (CFO) can adversely impact the performance of orthogonal frequency division multiplexing (OFDM) systems, since they can result in inter carrier interference and rotation of the signal constellation. In this paper, we propose an expectation conditional maximization (ECM) based algorithm for joint estimation of channel, PHN, and CFO in OFDM systems. We present the signal model for the estimation problem and derive the hybrid Cramer-Rao lower bound (HCRB) for the joint estimation problem. Next, we propose an iterative receiver based on an extended Kalman filter for joint data detection and PHN tracking. Numerical results show that, compared to existing algorithms, the performance of the proposed ECM-based estimator is closer to the derived HCRB and outperforms the existing estimation algorithms at moderate-to-high signal-to-noise ratio (SNR). In addition, the combined estimation algorithm and iterative receiver are more computationally efficient than existing algorithms and result in improved average uncoded and coded bit error rate (BER) performance

An Efficient Data-aided Synchronization in L-DACS1 for Aeronautical Communications

L-band Digital Aeronautical Communication System type-1 (L-DACS1) is an emerging standard that aims at enhancing air traffic management (ATM) by transitioning the traditional analog aeronautical communication systems to the superior and highly efficient digital domain. L-DACS1 employs modern and efficient orthogonal frequency division multiplexing (OFDM) modulation technique to achieve more efficient and higher data rate in comparison to the existing aeronautical communication systems. However, the performance of OFDM systems is very sensitive to synchronization errors. L-DACS1 transmission is in the L-band aeronautical channels that suffer from large interference and large Doppler shifts, which makes the synchronization for L-DACS more challenging. This paper proposes a novel computationally efficient synchronization method for L-DACS1 systems that offers robust performance. Through simulation, the proposed method is shown to provide accurate symbol timing offset (STO) estimation as well as fractional carrier frequency offset (CFO) estimation in a range of aeronautical channels. In particular, it can yield excellent synchronization performance in the face of a large carrier frequency offset.Comment: In the proceeding of International Conference on Data Mining, Communications and Information Technology (DMCIT