Constellation rotation and symbol detection for data-dependent superimposed training

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.