We successfully realized layered decoding for LDPC convolutional codes designed for application in high speed optical transmission systems. A relatively short code with 20% redundancy was FPGA-emulated with a Q-factor of 5.7dB at BER of 10 -15 .

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A novel joint symbol timing and carrier frequency offset (CFO) estimation algorithm is proposed for reduced-guard-interval coherent optical orthogonal frequency-division multiplexing (RGI-CO-OFDM) systems. The proposed algorithm is based on a constant amplitude zero autocorrelation (CAZAC) sequence weighted by a pseudo-random noise (PN) sequence. The symbol timing is accomplished by using only one training symbol of two identical halves, with the weighting applied to the second half. The special structure of the training symbol is also utilized in estimating the CFO. The performance of the proposed algorithm is demonstrated by means of numerical simulations in a 115.8-Gb/s 16-QAM RGI-CO-OFDM system. 1800-1805 (1997). 13. X. Zhou, X. Yang, R. Li, and K. Long, "Efficient joint carrier frequency offset and phase noise compensation scheme for high-speed coherent optical OFDM systems," J. Lightwave Technol. 31(11), 1755-1761 (2013). 14. Y. Huang, X. Zhang, and L. Xi, "Modified synchronization scheme for coherent optical OFDM systems," J. Opt.Commun. Netw. 5(6), 584-592 (2013 7350-7361 (2012). 16. X. Zhou, X. Chen, and K. Long, "Wide-range frequency offset estimation algorithm for optical coherent systems using training sequence," IEEE Photon.

This paper proposes a signal-to-noise ratio (SNR) estimator based on recurrent neural network (RNN) in optical fiber communication links. The proposed estimator jointly estimates the linear and nonlinear components of the SNR. The input features of the proposed estimator are carefully designed based on a combination of the lower quartile and entropy extracted from the received signal. The proposed input features do not require knowledge of the transmitted symbols. In the proposed SNR estimator, three different RNN models are investigated, namely simple RNN, gated recurrent units, and long shortterm memory. The overall computational complexity of the three models of the proposed estimator, including the feature extraction and RNN structures, are analyzed. Numerical results show that the three models of the proposed estimator provide a trade-off between the complexity of the RNN structure and estimation accuracy. Furthermore, the proposed estimator achieves a better SNR estimation accuracy and reduces the overall computational complexity compared to the literature.