A data-driven fiber channel modeling method based on deep learning (DL) is introduced in an optical communication system. In this study, bidirectional long short-term memory (BiLSTM) is selected from a diverse range of DL algorithms to perform fiber channel modeling for on—off keying and pulse amplitude modulation 4 signals. Compared with the conventional model-driven split-step Fourier (SSF)-based method, the proposed method yields similar results based on the comprehensive comparison of multiple characteristics associated with the generated optical signals, including the optical amplitude and phase waveforms in the time domain, optical spectrum components in the frequency domain, and eye diagrams after detection in the electrical domain. Additionally, the effects of multiple factors on the modeled fiber channel have also be investigated, including fiber length, fiber nonlinearity, dispersion, data pattern, pulse shaping, and sample rate. The satisfactory fitting results and acceptable mean square errors indicate that the approximate transfer function of the fiber channel is learned by the BiLSTM. Moreover, compared with repetitive iteration SSF, the computing time is significantly reduced by the BiLSTM owing to its independence on fiber length and insensitivity to data size and launch power. Our aim is to demonstrate the BiLSTM is comparable with the conventional model-driven SSF-based method for direct-detection optical fiber system. We think the proposed method could be a supplementary technique that can be used for the existing simulation system and could also be a potential option for future simulation methods.

Nanophotonic devices have enabled electromagnetic manipulation with unprecedented accuracy and functionalities. With the increased functionality requirements of such devices and the progress of fabrication technologies, trends toward miniaturization and multi-functionality have emerged, making forward response prediction and inverse design increasingly complicated. In this paper, a physical information-embedded deep learning model for flexible forward prediction and multifunctional inverse design is proposed. Besides the overwhelming advantage of computing efficiency over conventional iterative optimization methods, the proposed method exhibits strong generalization on different wavelengths and polarizations by embedding the corresponding physical information into the model through effective sparse parameter method (SPM). For the forward prediction, responses can be predicted among the 1300-1550 nm wavelength band and two polarizations (TM/TE) with high accuracy. For the inverse design, with the assistance of forward prediction models and broadband training methods, multi-dimensional information for the target can be considered simultaneously to retrieve the satisfactory desired designs topologies in an unsupervised way. The multi-functionality of inverse design is demonstrated by implementing wavelength demultiplexers with small crosstalk and power splitters with arbitrary splitting ratios on a 250 nm broadband wavelength in a compact footprint of 2 × 2 μm ² without additional heavy training of the model.

This paper describes the implications of the new trends in web development languages on log file analysis for e-commerce. The new trends in Software Development and the available Web Analytics technologies are explained. The focus is placed on the diversifications introduced to the traces left on the system that by Rich Internet Applications (RIAs). Finally a novel hybrid solution is proposed that is based on the junction of log files with operational data and page tagging, which allows even exacter measurements of customer behavior. It allows a customization of the Analysis Tool and survives the shift of the technologies.

We present analytical results on the average probability of error (PER) performance of an optically pre-amplified pulse-position modulation (PPM) receiver under Malaga- $\mathcal{M}$ fading. The results are in the form of a finite sum whose number of terms depends on the PPM modulation order and the noise modes of the amplifier, enabling the efficient calculation of the average PER. In addition, we utilized the presented analysis to evaluate the performance of a equal-gain-combining (EGC) diversity receiver that operates in conjunction with optical amplification and PPM. The results show that the utilization of diverse and relatively low PPM orders achieves a drastic reduction in the average PER.