Spread Spectrum Orthogonalization of Superimposed Training Signals in OFDM System’s Simulation

in this paper, we discuss the simulation for a paper titled Spread Spectrum Orthogonalization of Superimposed training Signals in OFDM Systems. In the paper, the writer propound a new technique named Superimposed Training (ST) for Orthogonal Frequency Division Multiplexing (OFDM) systems. OFDM is a technique of separating data and training signals in orthogonal code domains which is to alleviate the obstacles among one another. The data signals is divided into separated bins, which are separated using orthogonal codes and in code domain, it is multiplexed. Next, the spread training signal is combined with the new data signal. This proposal called Spread Spectrum Orthogonalization (SSO). For the simulation, we will measure noise effect on the signal in the channel. We will estimate the ratio of mean square error, signal to noise, and bit error rate.

Superimposed Training, orthogonal Frequency Division Multiplexing (OFDM), Spread Spectrum, Signal to Noise ratio (SNR), Mean Square Error (MSE).