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
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;
Journal_Title :
Electronics Letters
DOI :
10.1049/el.2014.1681
