DocumentCode :
78634
Title :
Constellation rotation and symbol detection for data-dependent superimposed training
Author :
Gaoqi Dou ; CongYing Li ; Jun Gao ; Fuliang Guo
Author_Institution :
Dept. of Commun. Eng., Naval Univ. of Eng., Wuhan, China
Volume :
50
Issue :
25
fYear :
2014
fDate :
12 4 2014
Firstpage :
1939
Lastpage :
1940
Abstract :
The problem of symbol misidentification (SMI) for data-dependent superimposed training (DDST) is considered. The constraint conditions on the discrete Fourier transform matrix are derived and constellation rotation (CR) at the transmitter to avoid the SMI is proposed. Simulation results show that the DDST with CR can eliminate the symbol error floor and yield better detection performance than the original one.
Keywords :
channel estimation; discrete Fourier transforms; learning (artificial intelligence); matrix algebra; transmitters; CR; DDST; DFT matrix; SMI; channel estimation; constellation rotation; data-dependent superimposed training; discrete Fourier transform matrix; symbol detection; symbol error floor elimination; symbol misidentification; transmitter;
fLanguage :
English
Journal_Title :
Electronics Letters
Publisher :
iet
ISSN :
0013-5194
Type :
jour
DOI :
10.1049/el.2014.1681
Filename :
6975769
Link To Document :
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