NDLI: Efficient near optimal maximum likelihood symbol timing recovery in digital modems

Content Provider	IEEE Xplore Digital Library
Author	Tuukkanen, V. Vesma, J. Renfors, M.
Copyright Year	1997
Description	Author affiliation: Digital Media Inst., Tampere Univ. of Technol., Finland (Tuukkanen, V.)
Abstract	Maximum likelihood (ML) estimation theory provides a general framework for developing optimum timing recovery schemes for digital communication systems. Feedforward schemes based on ML-theory have been shown to have rapid acquisition characteristics. Another important trend is to use digital interpolation for symbol timing adjustment. The so-called Farrow (1988) structure has become popular in this context because it offers an efficient and flexible realization structure for such an interpolation task. In a previous paper Vesma and Tuukkanen (see Proc. IEEE NORSIG 96, Espoo, Finland, p.183-6, 1997) proposed a new feedforward timing estimation scheme which utilizes the Farrow structure efficiently. The scheme is based on a low-order polynomial approximation for the log-likelihood function and also the derivative of this function is available with a small additional computational cost. The performance of the new timing recovery technique was found to be close to ideal even with short symbol block-lengths which is very important in mobile systems. In this paper, we show that this new scheme gives much better results when optimized training sequences are used. The scheme works also with severe multipath distortion channel.
Starting Page	825
Ending Page	829
File Size	464865
Page Count	5
File Format	PDF
ISBN	0780338715
DOI	10.1109/PIMRC.1997.627001
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-09-01
Publisher Place	Finland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Timing Modems Interpolation Sampling methods Filters Maximum likelihood estimation Synchronization Clocks Signal sampling Polynomials
Content Type	Text
Resource Type	Article

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