WHAT IS THE PRICE PAID FOR SUPERIMPOSED TRAINING IN OFDM? ∗

Orthogonal Frequency Division Multiplexing (OFDM) transmission with superimposed training is considered in this paper. The superimposed training scheme is promoted for its high bandwidth efficiency, low computational complexity, and possibly improved power amplifier (PA) efficiency. Channel equalization is also straightforward thanks to the OFDM structure. By analyzing the peak-to-average power ratio (PAR) of the superimposed OFDM signal and utilizing a peak power constraint, we demonstrate that it is possible to lose a little in the information signal power, but gain a lot in the power that is devoted to channel sounding. 1

Superimposed training for single carrier transmission in future mobile communications

The amount of wireless devices and wireless traffic has been increasing exponentially for the last ten years. It is forecasted that the exponential growth will continue without saturation till 2020 and probably further. So far, network vendors and operators have tackled the problem by introducing new evolutions of cellular macro networks, where each evolution has increased the physical layer spectral efficiency. Unfortunately, the spectral efficiency of the physical layer is achieving the Shannon-Hartley limit and does not provide much room for improvement anymore. However, considering the overhead due to synchronization and channel estimation reference symbols in the context of physical layer spectral efficiency, we believe that there is room for improvement. In this thesis, we will study the potentiality of superimposed training methods, especially data-dependent superimposed training, to boost the spectral efficiency of wideband single carrier communications even further. The main idea is that with superimposed training we can transmit more data symbols in the same time duration as compared to traditional time domain multiplexed training. In theory, more data symbols means more data bits which indicates higher throughput for the end user. In practice, nothing is free. With superimposed training we encounter self-interference between the training signal and the data signal. Therefore, we have to look for iterative receiver structures to separate these two or to estimate both, the desired data signal and the interfering component. In this thesis, we initiate the studies to find out if we truly can improve the existing systems by introducing the superimposed training scheme. We show that in certain scenarios we can achieve higher spectral efficiency, which maps directly to higher user throughput, but with the cost of higher signal processing burden in the receiver. In addition, we provide analytical tools for estimating the symbol or bit error ratio in the receiver with a given parametrization. The discussion leads us to the conclusion that there still remains several open topics for further study when looking for new ways of optimizing the overhead of reference symbols in wireless communications. Superimposed training with data-dependent components may prove to provide extra throughput gain. Furthermore, the superimposed component may be used for, e.g., improved synchronization, low bit-rate signaling or continuous tracking of neighbor cells. We believe that the current systems could be improved by using the superimposed training collectively with time domain multiplexed training

Adaptive Equalization Based On Watermarking

This paper presents an adaptive equalization method based on the use of a watermark as reference signal. Instead of interrupting communication periodically to transmit training sequences, a watermark is transmitted continuously through the channel along with the user signal. By analyzing the received watermark, the equalization filter is continuously adapted to match the channel characteristics. When dealing with correlated user signals, a prediction-error filter is introduced to whiten the equalizer output before comparing it to the reference signal (the watermark), allowing for the appropriate inversion of the channel transfer function. Simulation results are presented to demonstrate the viability of this approach. © 2006 SBrT.743748Haykin, S., (1996) Adaptive filter theory, , third edition, Prentice HallL. de C. T. Gomes, P.Cano, E. Gómez, M. Bonnet, E. Batlle, Audio watermarking and fingerprinting: for which applications?, Journal of New Music Research, 32 .1, 2003L. de C. T. Gomes, E. Gómez, N. Moreau, Resynchronization methods for audio watermarking, Proceedings of 111th AES Convention, New York, USA, November 2001E. Gómez, P. Cano, L. de C. T. Gomes, E. Batlle, M. Bonnet, Mixed watermarking-fingerprinting approach for integrity verification of audio recordings, Proceedings of IEEE International Telecommunications Symposium, Natal, Brazil, September 2002L. de C. T. Gomes, M. Mboup, M. Bonnet, Cyclostationarity-based audio watermarking with private and public hidden data, 109th Convention of Audio Engineering Society, Los Angeles, September 2000Kim, H.J., Audio watermarking techniques (2003) Pacific Rim Workshop on Digital Steganography, , Kyushu Institute of Technology, Kitakyushu, Japan, JulyMoulin, P., Koetter, R., Data-hiding codes (2005) Proceedings of the IEEE, 93 (12). , DecemberA. R. Varma, L. L. H. Andrew, C. R. N. Athaudage, J. H. Manton, Iterative algorithms for channel identification using superimposed pilots, Australian Communications Theory Workshop, February 2004Tugnait, J.K., Luo, W., On channel estimation using superimposed training and first-order statistics (2003) IEEE Communications Letters, 7 (9). , SeptemberTugnait, J.K., Meng, X., Synchronization of superimposed training for channel estimation (2004) Proc. 2004 IEEE International Conf. on Acoustics, Speech, Signal Processing, 4, pp. 853-856. , Montreal, Canada, MayN. Chen, and G. T. Zhou, What is the price paid for superimposed training in OFDM?, Proc. 2004 IEEE International Conf. on Acoustics, Speech, Signal Processing, pp. 421-424, Montreal, Canada, May 200