Deep Learning Assisted Pre-equalization Scheme for Faster-than-Nyquist Optical Wireless Communications

Faster-than-Nyquist technology introduces additional inter-symbol interference when improving the data rate and spectrum utilization. A deep-learning based pre-equalization scheme is proposed to reduce the inter-symbol interference. The bit error rate performance under different acceleration factors is analyzed. Monte Carlo simulation results show that the bit error rate performance can be improved by 1dB, 5.1dB, 6. 0dB and 8. 6dB when the acceleration factor is 0.9, 0.8, 0.7 and 0.6, respectively.