Factors shaping the evolution of electronic documentation systems

The main goal is to prepare the space station technical and managerial structure for likely changes in the creation, capture, transfer, and utilization of knowledge. By anticipating advances, the design of Space Station Project (SSP) information systems can be tailored to facilitate a progression of increasingly sophisticated strategies as the space station evolves. Future generations of advanced information systems will use increases in power to deliver environmentally meaningful, contextually targeted, interconnected data (knowledge). The concept of a Knowledge Base Management System is emerging when the problem is focused on how information systems can perform such a conversion of raw data. Such a system would include traditional management functions for large space databases. Added artificial intelligence features might encompass co-existing knowledge representation schemes; effective control structures for deductive, plausible, and inductive reasoning; means for knowledge acquisition, refinement, and validation; explanation facilities; and dynamic human intervention. The major areas covered include: alternative knowledge representation approaches; advanced user interface capabilities; computer-supported cooperative work; the evolution of information system hardware; standardization, compatibility, and connectivity; and organizational impacts of information intensive environments

Geometric Constellation Shaping for Fiber-Optic Channels via End-to-End Learning

End-to-end learning has become a popular method to optimize a constellation shape of a communication system. When the channel model is differentiable, end-to-end learning can be applied with conventional backpropagation algorithm for optimization of the shape. A variety of optimization algorithms have also been developed for end-to-end learning over a non-differentiable channel model. In this paper, we compare gradient-free optimization method based on the cubature Kalman filter, model-free optimization and backpropagation for end-to-end learning on a fiber-optic channel modeled by the split-step Fourier method. The results indicate that the gradient-free optimization algorithms provide a decent replacement to backpropagation in terms of performance at the expense of computational complexity. Furthermore, the quantization problem of finite bit resolution of the digital-to-analog and analog-to-digital converters is addressed and its impact on geometrically shaped constellations is analysed. Here, the results show that when optimizing a constellation with respect to mutual information, a minimum number of quantization levels is required to achieve shaping gain. For generalized mutual information, the gain is maintained throughout all of the considered quantization levels. Also, the results implied that the autoencoder can adapt the constellation size to the given channel conditions

Deep learning for interference cancellation in non-orthogonal signal based optical communication systems

Non-orthogonal waveforms are groups of signals, which improve spectral efficiency but at the cost of interference. A recognized waveform, termed spectrally efficient frequency division multiplexing (SEFDM), which was a technique initially proposed for wireless systems, has been extensively studied in 60 GHz millimeter wave communications, optical access network design and long haul optical fiber transmission. Experimental demonstrations have shown the advantages of SEFDM in its bandwidth saving, data rate improvement, power efficiency improvement and transmission distance extension compared to conventional orthogonal communication techniques. However, the achieved success of SEFDM is at the cost of complex signal processing for the mitigation of the self-created inter carrier interference (ICI). Thus, a low complexity interference cancellation approach is in urgent need. Recently, deep learning has been applied in optical communication systems to compensate for linear and non-linear distortions in orthogonal frequency division multiplexing (OFDM) signals. The multiple processing layers of deep neural networks (DNN) can simplify signal processing models and can efficiently solve un-deterministic problems. However, there are no reports on the use of deep learning to deal with interference in non-orthogonal signals. DNN can learn complex interference features using backpropagation mechanism. This work will present our investigations on the performance improvement of interference cancellation for the non-orthogonal signal using various deep neural networks. Simulation results show that the interference within SEFDM signals can be mitigated efficiently via using properly designed neural networks. It also indicates a high correlation between neural networks and signal waveforms. It verifies that in order to achieve the optimal performance, all the neurons at each layer have to be connected. Partially connected neural networks cannot learn complete interference and therefore cannot recover signals efficiently. This work paves the way for the research of simplifying neural networks design via signal waveform optimization

Machine learning for optical fiber communication systems: An introduction and overview

Optical networks generate a vast amount of diagnostic, control and performance monitoring data. When information is extracted from this data, reconfigurable network elements and reconfigurable transceivers allow the network to adapt both to changes in the physical infrastructure but also changing traffic conditions. Machine learning is emerging as a disruptive technology for extracting useful information from this raw data to enable enhanced planning, monitoring and dynamic control. We provide a survey of the recent literature and highlight numerous promising avenues for machine learning applied to optical networks, including explainable machine learning, digital twins and approaches in which we embed our knowledge into the machine learning such as physics-informed machine learning for the physical layer and graph-based machine learning for the networking layer

Cognitive and Autonomous Software-Defined Open Optical Networks

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