In data-dependent superimposed training (DDST) system, the data sequence subtracts a data-dependent sequence before transmission. The receiver cannot correctly find the unknown term which causes an error floor at high SNR. In this thesis, we list some helpful conditions to enhance the performance for precoding design in DDST system, and analyze the major cause of data misidentification by singular value decomposition (SVD) method. Finally, we propose a precoding matrix based on [C.-P. Li and W.-C. Huang, ¡§A constructive representation for the Fourier dual of the Zadoff¡VChu sequences,¡¨ IEEE Trans. Inf. Theory, vol. 53, no. 11, pp. 4221¡Ð4224, Nov. 2007]. The precoding matrix is constructed by an inverse discrete Fourier transform (IDFT) matrix and a diagonal matrix with the elements consist of an arbitrary perfect sequence. The proposed method satisfies these conditions and simulation results show that the data identification problem is solved.