Compressed sensing theory-based channel estimation for optical orthogonal frequency division multiplexing communication system

Due to the spare multipath property of the channel, a channel estimation method, which is based on partial superimposed training sequence and compressed sensing theory, is proposed for line of sight optical orthogonal frequency division multiplexing communication systems. First, a continuous training sequence is added at variable power ratio to the cyclic prefix of orthogonal frequency division multiplexing symbols at the transmitter prior to transmission. Then the observation matrix of compressed sensing theory is structured by the use of the training symbols at receiver. Finally, channel state information is estimated using sparse signal reconstruction algorithm. Compared to traditional training sequences, the proposed partial superimposed training sequence not only improves the spectral efficiency, but also reduces the influence to information symbols. In addition, compared with classical least squares and linear minimum mean square error methods, the proposed compressed sensing theory based channel estimation method can improve both the estimation accuracy and the system performance. Simulation results are given to demonstrate the performance of the proposed method.