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