1,948 research outputs found
Machine learning for fiber nonlinearity mitigation in long-haul coherent optical transmission systems
Fiber nonlinearities from Kerr effect are considered as major constraints for enhancing the transmission capacity in current optical transmission systems. Digital nonlinearity compensation techniques such as digital backpropagation can perform well but require high computing resources. Machine learning can provide a low complexity capability especially for high-dimensional classification problems. Recently several supervised and unsupervised machine learning techniques have been investigated in the field of fiber nonlinearity mitigation. This paper offers a brief review of the principles, performance and complexity of these machine learning approaches in the application of nonlinearity mitigation
Coherent 100G Nonlinear Compensation with Single-Step Digital Backpropagation
Enhanced-SSFM digital backpropagation (DBP) is experimentally demonstrated
and compared to conventional DBP. A 112 Gb/s PM-QPSK signal is transmitted over
a 3200 km dispersion-unmanaged link. The intradyne coherent receiver includes
single-step digital backpropagation based on the enhanced-SSFM algorithm. In
comparison, conventional DBP requires twenty steps to achieve the same
performance. An analysis of the computational complexity and structure of the
two algorithms reveals that the overall complexity and power consumption of DBP
are reduced by a factor of 16 with respect to a conventional implementation,
while the computation time is reduced by a factor of 20. As a result, the
proposed algorithm enables a practical and effective implementation of DBP in
real-time optical receivers, with only a moderate increase of the computational
complexity, power consumption, and latency with respect to a simple
feed-forward equalizer for dispersion compensation.Comment: This work has been presented at Optical Networks Design & Modeling
(ONDM) 2015, Pisa, Italy, May 11-14, 201
Advanced DSP for coherent optical fiber communication
In this paper, we provide an overview of recent progress on advanced digital signal processing (DSP) techniques for high-capacity long-haul coherent optical fiber transmission systems. Not only the linear impairments existing in optical transmission links need to be compensated, but also, the nonlinear impairments require proper algorithms for mitigation because they become major limiting factors for long-haul large-capacity optical transmission systems. Besides the time domain equalization (TDE), the frequency domain equalization (FDE) DSP also provides a similar performance, with a much-reduced computational complexity. Advanced DSP also plays an important role for the realization of space division multiplexing (SDM). SDM techniques have been developed recently to enhance the system capacity by at least one order of magnitude. Some impressive results have been reported and have outperformed the nonlinear Shannon limit of the single-mode fiber (SMF). SDM introduces the space dimension to the optical fiber communication. The few-mode fiber (FMF) and multi-core fiber (MCF) have been manufactured for novel multiplexing techniques such as mode-division multiplexing (MDM) and multi-core multiplexing (MCM). Each mode or core can be considered as an independent degree of freedom, but unfortunately, signals will suffer serious coupling during the propagation. Multi-input−multi-output (MIMO) DSP can equalize the signal coupling and makes SDM transmission feasible. The machine learning (ML) technique has attracted worldwide attention and has been explored for advanced DSP. In this paper, we firstly introduce the principle and scheme of coherent detection to explain why the DSP techniques can compensate for transmission impairments. Then corresponding technologies related to the DSP, such as nonlinearity compensation, FDE, SDM and ML will be discussed. Relevant techniques will be analyzed, and representational results and experimental verifications will be demonstrated. In the end, a brief conclusion and perspective will be provided
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