Data-Aided Symbol Timing Estimation for Linear Modulation |
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Authors: | Axel Gesell Johannes B. Huber Berthold Lankl Georg Sebald |
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Affiliation: | 1. Information and Communication Networks, Siemens AG, Hofmannstr. 51, D-81359 München, Germany.;2. Chair of Information Transmission, University Erlangen-Nürnberg, Cauerstr. 7/NT, D-91058 Erlangen, Germany.;1. School of Physics and Electronic Information Engineering, Henan Polytechnic University, China;2. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China;3. School of Information and Engineering, Zhengzhou University, China;4. Wireless Telecommunications Research Group, Federal University of Ceara, Fortaleza, Brazil;2. AIRBUS Defence and Space, Elancourt, France;3. SINTEF Digital, Trondheim, Norway |
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Abstract: | Timing phase estimation (TPE) plays the key role in feedforward (FF) symbol timing.For reasons of performance often data-aided (DA) methods are preferred. But frequently, they turn out to be critical with respect to theimplementation and the spectrum efficiency (due to the required overhead).This paper presents three DA TPE methods for quadrature pulse amplitude modulation (PAM). In spite of their very low complexity, these methods closelyapproach the theoretical limit for timing estimation with respect to the estimation variance, even at low SNR. This enables power efficient transmission.Further, they employ a CAZAC (constant amplitude, zero auto-correlation)sequence as training-sequence (TS), or a sequence with similar correlationproperties. Since such sequences are suited for almost all DA receiver tasks, a high spectrum efficiency can be obtained by the use of a single TS. A generalization of the proposed methods for DA TPE withrespect to the choice of the TS is also shown. The presented methods can be applied to noncoherent receivers. They are suited for high data-rate applications, since they can work with an oversampling factor of 2. |
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Keywords: | Timing phase estimation Feedforward symbol timing recovery Data-aided Training sequence Maximum-likelihood |
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