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Register a new userDiscrete FRFT-Based Frame and Frequency Synchronization for Coherent Optical Systems
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Added by
Oluyemi Omomukuyo
Publication date
2018
fields
Electronic Engineering
and research's language is
English
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A joint frame and carrier frequency synchronization algorithm for coherent optical systems, based on the digital computation of the fractional Fourier transform (FRFT), is proposed. The algorithm utilizes the characteristics of energy centralization of chirp signals in the FRFT domain, together with the time and phase shift properties of the FRFT. Chirp signals are used to construct a training sequence (TS), and fractional cross-correlation is employed to define a detection metric for the TS, from which a set of equations can be obtained. Estimates of both the timing offset and carrier frequency offset (CFO) are obtained by solving these equations. This TS is later employed in a phase-dependent decision-directed least-mean square algorithm for adaptive equalization. Simulation results of a 32-Gbaud coherent polarization division multiplexed Nyquist system show that the proposed scheme has a wide CFO estimation range and accurate synchronization performance even in poor optical signal-to-noise ratio conditions.
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We propose an algorithm for carrying out joint frame and frequency synchronization in reduced-guard-interval coherent optical orthogonal frequency division multiplexing (RGI-CO-OFDM) systems. The synchronization is achieved by using the same training symbols (TS) employed for training-aided channel estimation (TA-CE), thereby avoiding additional training overhead. The proposed algorithm is designed for polarization division multiplexing (PDM) RGI-CO-OFDM systems that use the Alamouti-type polarization-time coding for TA-CE. Due to their optimal TA-CE performance, Golay complementary sequences have been used as the TS in the proposed algorithm. The frame synchronization is accomplished by exploiting the cross-correlation between the received TS from the two orthogonal polarizations. The arrangement of the TS is also used to estimate the carrier frequency offset. Simulation results of a PDM RGI-CO-OFDM system operating at 238.1 Gb/s data rate (197.6-Gb/s after coding), with a total overhead of 9.2% (31.6% after coding), show that the proposed scheme has accurate synchronization, and is robust to linear fiber impairments.
In burst-mode communication systems, the quality of frame synchronization (FS) at receivers significantly impacts the overall system performance. To guarantee FS, an extreme learning machine (ELM)-based synchronization method is proposed to overcome the nonlinear distortion caused by nonlinear devices or blocks. In the proposed method, a preprocessing is first performed to capture the coarse features of synchronization metric (SM) by using empirical knowledge. Then, an ELM-based FS network is employed to reduce systems nonlinear distortion and improve SMs. Experimental results indicate that, compared with existing methods, our approach could significantly reduce the error probability of FS while improve the performance in terms of robustness and generalization.
In this work, we address the important question of adaptability of artificial neural networks (NNs) used for impairment mitigation in optical transmission systems. We demonstrate that by using well-developed techniques based on the concept of transfer learning, we can efficaciously retrain NN-based equalizers to adapt changes in the transmission system using just a fraction of the initial training data and epochs. We evaluate the potential of transfer learning to adapt the NN to changes in the launch powers, modulation formats, symbol rates, or even fiber plants (different fiber types and lengths). The numerical examples utilize the recently introduced NN equalizer consisting of a convolutional layer coupled with bi-directional long-short term memory (biLSTM) recurrent NN element. Our analysis focuses on long-haul coherent optical transmission systems for two types of fibers: the standard single-mode fiber (SSMF) and the TrueWave Classic (TWC) fiber. We underline the specific peculiarities that occur when transferring the learning in coherent optical communication systems and draw the limits for the transfer learning efficiency. Our results demonstrate the effectiveness of transfer learning for the fast adaptation of NN architectures to different transmission regimes and scenarios, paving the way for engineering flexible and universal solutions for nonlinearity mitigation.
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.
The requirement of high spectrum efficiency puts forward higher requirements on frame synchronization (FS) in wireless communication systems. Meanwhile, a large number of nonlinear devices or blocks will inevitably cause nonlinear distortion. To avoid the occupation of bandwidth resources and overcome the difficulty of nonlinear distortion, an extreme learning machine (ELM)-based network is introduced into the superimposed training-based FS with nonlinear distortion. Firstly, a preprocessing procedure is utilized to reap the features of synchronization metric (SM). Then, based on the rough features of SM, an ELM network is constructed to estimate the offset of frame boundary. The analysis and experiment results show that, compared with existing methods, the proposed method can improve the error probability of FS and bit error rate (BER) of symbol detection (SD). In addition, this improvement has its robustness against the impacts of parameter variations.
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