Nonlinearity compensation algorithm and soft-decision forward error correction (FEC) are considered as key technologies for future high-capacity and long-haul optical transmission system. In this report, we experimentally demonstrate the following three benefits brought by low complexity perturbation back-propagation nonlinear compensation algorithm in 224Gb/s DP-16QAM transmission over large-Aeff pure silica core fiber; (1) improvement of pre-FEC bit error ratio, (2) reshaping noise distribution to more Gaussian, and (3) reduction of cycle slip probability.

Compensation for the nonlinear systems represented by polynomials involves polynomial inverse. In this paper, a new algorithm is proposed that gives the baseband polynomial inverse with a limited order. The algorithm employs orthogonal basis that is predetermined from the distribution of input signal and finds the coefficients of the inverse polynomial to minimize the mean square error. Compared with the well established p-th order inverse method, the proposed method can suppress the distortions better including higher order distortions. It is also extended to obtain memory polynomial inverse through a feedback-configured structure. Both numerical simulations and experimental results demonstrate that the proposed algorithm can provide good performance for compensating the nonlinear systems represented by baseband polynomials.

For effective use of the frequency band, carrier superposing (common band) technique has been introduced to satellite communication systems. On the other hand, satellite's TWTA (Traveling Wave Tube Amplifier) should be operated near its saturation level for power efficiency. However, the TWTA nonlinearity characteristics around that level causes interference in carrier superposing systems. Therefore in this paper, a post-compensation technique for TWTA nonlinear distortion is introduced and verified for practical use in a carrier superposed Point to Point satellite communication system which adopts interference canceller. Simulation results show that it is possible to reduce the bit error rate degradation over the entire range, especially at nonlinear operating point.

The heterodyne laser interferometer acts as an ultra-precise measurement apparatus in semiconductor manufacture. However the periodical nonlinearity property caused from frequency cross-talk is an obstacle to improve the high measurement accuracy in nanometer scale. In order to minimize the nonlinearity error of the heterodyne interferometer, we propose a frequency cross-talk compensation algorithm using an artificial intelligence method. The feedforward neural network trained by back-propagation compensates the nonlinearity error and regulates to minimize the difference with the reference signal. With some experimental results, the improved accuracy is proved through comparison with the position value from a capacitive displacement sensor.