NDLI: Equalization of ATSC DTV Systems Based on Space-Time Signal Processing for Mobile Reception

Content Provider	IEEE Xplore Digital Library
Author	Hae-Sock Oh Dong Seog Han Chae-Hyun Lim
Copyright Year	2006
Description	Author affiliation: Sch. of Electr. Eng. & Comput. Sci., Kyungpook Nat. Univ. (Hae-Sock Oh; Dong Seog Han; Chae-Hyun Lim)
Abstract	A joint operation of the Capon and the least mean square (LMS) beamformers is proposed to get reliable reception performance for the advanced television system committee's (ATSC's) digital television (DTV) system. The proposed beamformer called a Capon and LMS (CLMS) beamformer provides an initial weight set for the operation of the LMS beamformer during the training sequence. The Capon beamformer is operated during the payload data period. By the way, the Capon beamformer could be unstable because of frequent changes of the steering beam direction in mobile conditions. To get a stable operation in mobile conditions with the CLMS beamformer, the signal-to-noise ratio (SNR) is estimated to control the operation by utilizing the Viterbi decoder output. When the SNR level is below the threshold of visibility (TOV), the Capon beamformer is operated to get a fast initialization of the LMS beamformer. When the receiver is moving with a speed of 60 km/h for 700 MHz band, the successful reception probability of the CLMS beamformer based on SNR estimation is 76% compared to 60% for that of the conventional CLMS beamformer
Sponsorship	IEEE COM
Starting Page	1
Ending Page	5
File Size	142763
Page Count	5
File Format	PDF
ISBN	1424403294
DOI	10.1109/PIMRC.2006.254088
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-09-11
Publisher Place	Finland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Digital TV Signal processing Least squares approximation Viterbi algorithm Decoding Equalizers Antennas and propagation Performance analysis Payloads Signal to noise ratio
Content Type	Text
Resource Type	Article

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