NDLI: Manufacturing training symbols from future bits: a novel approach to estimate time-varying flat-fading channels

Content Provider	IEEE Xplore Digital Library
Author	Zhou, H. Collins, O.M.
Copyright Year	2005
Description	Author affiliation: Dept. of Electr. Eng., Notre Dame Univ., IN, USA (Zhou, H.; Collins, O.M.)
Abstract	This paper presents a state generated training symbol (SGTS) algorithm as a channel estimation scheme for sequence detector under time-varying flat-fading channels. The basic idea of SGTS is that data-aided unknown parameters estimation can be embedded into the structure of the Viterbi algorithm. By using a systematic convolutional code, the unknown parameter estimation in SGTS will use a future training sequence manufactured by the current state before the actual Viterbi decoding process, which is distinct from the well-known per-survivor processing (PSP) algorithm. Simulation results show that the novel SGTS-based sequence detector has similar performance with lower computation load compared with the PSP-based one. Furthermore, SGTS can coordinate with PSP. The resulting sequence detector achieves significant performance improvements with better channel estimation, especially under fast fading channels.
Starting Page	410
Ending Page	414
File Size	837261
Page Count	5
File Format	PDF
ISBN	0780388674
DOI	10.1109/SPAWC.2005.1506057
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-06-05
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Manufacturing Viterbi algorithm Fading Maximum likelihood decoding Parameter estimation Convolutional codes State estimation Detectors Channel estimation Maximum likelihood estimation
Content Type	Text
Resource Type	Article

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