Importance Sampling for Dispersion-managed Solitons

The dispersion-managed nonlinear Schrödinger (DMNLS) equation governs the long-term dynamics of systems which are subject to large and rapid dispersion variations. We present a method to study large, noise-induced amplitude and phase perturbations of dispersion-managed solitons. The method is based on the use of importance sampling to bias Monte Carlo simulations toward regions of state space where rare events of interest—large phase or amplitude variations—are most likely to occur. Implementing the method thus involves solving two separate problems: finding the most likely noise realizations that produce a small change in the soliton parameters, and finding the most likely way that these small changes should be distributed in order to create a large, sought-after amplitude or phase change. Both steps are formulated and solved in terms of a variational problem. In addition, the first step makes use of the results of perturbation theory for dispersion-managed systems recently developed by the authors. We demonstrate this method by reconstructing the probability density function of amplitude and phase deviations of noise-perturbed dispersion-managed solitons and comparing the results to those of the original, unaveraged system

Importance Sampling for Dispersion-managed Solitons

The dispersion-managed nonlinear Schrödinger (DMNLS) equation governs the long-term dynamics of systems which are subject to large and rapid dispersion variations. We present a method to study large, noise-induced amplitude and phase perturbations of dispersion-managed solitons. The method is based on the use of importance sampling to bias Monte Carlo simulations toward regions of state space where rare events of interest—large phase or amplitude variations—are most likely to occur. Implementing the method thus involves solving two separate problems: finding the most likely noise realizations that produce a small change in the soliton parameters, and finding the most likely way that these small changes should be distributed in order to create a large, sought-after amplitude or phase change. Both steps are formulated and solved in terms of a variational problem. In addition, the first step makes use of the results of perturbation theory for dispersion-managed systems recently developed by the authors. We demonstrate this method by reconstructing the probability density function of amplitude and phase deviations of noise-perturbed dispersion-managed solitons and comparing the results to those of the original, unaveraged system

Importance sampling for thermally induced switching and non-switching probabilities in spin-torque magnetic nanodevices

Spin-transfer torque magnetoresistive random access memory is a potentially transformative technology in the non-volatile memory market. Its viability depends, in part, on one's ability to predictably induce or prevent switching; however, thermal fluctuations cause small but important errors in both the writing and reading processes. Computing these very small probabilities for magnetic nanodevices using naive Monte Carlo simulations is essentially impossible due to their slow statistical convergence, but variance reduction techniques can offer an effective way to improve their efficiency. Here, we provide an illustration of how importance sampling can be efficiently used to estimate low read and write soft error rates of macrospin and coupled-spin systems.Comment: 11 pages, 14 figure

Digital Signal Processing Techniques For Coherent Optical Communication

Coherent detection with subsequent digital signal processing (DSP) is developed, analyzed theoretically and numerically and experimentally demonstrated in various fiber-optic transmission scenarios. The use of DSP in conjunction with coherent detection unleashes the benefits of coherent detection which rely on the preservation of full information of the incoming field. These benefits include high receiver sensitivity, the ability to achieve high spectral-efficiency and the use of advanced modulation formats. With the immense advancements in DSP speeds, many of the problems hindering the use of coherent detection in optical transmission systems have been eliminated. Most notably, DSP alleviates the need for hardware phase-locking and polarization tracking, which can now be achieved in the digital domain. The complexity previously associated with coherent detection is hence significantly diminished and coherent detection is once again considered a feasible detection alternative. In this thesis, several aspects of coherent detection (with or without subsequent DSP) are addressed. Coherent detection is presented as a means to extend the dispersion limit of a duobinary signal using an analog decision-directed phase-lock loop. Analytical bit-error ratio estimation for quadrature phase-shift keying signals is derived. To validate the promise for high spectral efficiency, the orthogonal-wavelength-division multiplexing scheme is suggested. In this scheme the WDM channels are spaced at the symbol rate, thus achieving the spectral efficiency limit. Theory, simulation and experimental results demonstrate the feasibility of this approach. Infinite impulse response filtering is shown to be an efficient alternative to finite impulse response filtering for chromatic dispersion compensation. Theory, design considerations, simulation and experimental results relating to this topic are presented. Interaction between fiber dispersion and nonlinearity remains the last major challenge deterministic effects pose for long-haul optical data transmission. Experimental results which demonstrate the possibility to digitally mitigate both dispersion and nonlinearity are presented. Impairment compensation is achieved using backward propagation by implementing the split-step method. Efficient realizations of the dispersion compensation operator used in this implementation are considered. Infinite-impulse response and wavelet-based filtering are both investigated as a means to reduce the required computational load associated with signal backward-propagation. Possible future research directions conclude this dissertation

An integrated view on monitoring and compensation for dynamic optical networks: from management to physical layer

A vertical perspective, ranging from management and routing to physical layer options, concerning dynamic network monitoring and compensation of impairments (M&C), is given. Feasibility, reliability, and performance improvements on reconfigurable transparent networks are expected to arise from the consolidated assessment of network management and control specifications, as a more accurate evaluation of available M&C techniques. In the network layer, physical parameters aware algorithms are foreseen to pursue reliable network performance. In the physical layer, some new M&C methods were developed and rating of the state-of-the-art reported in literature is given. Optical monitoring implementation and viability is discussed.Publicad

Analysis and equalization of data-dependent jitter

Data-dependent jitter limits the bit-error rate (BER) performance of broadband communication systems and aggravates synchronization in phase- and delay-locked loops used for data recovery. A method for calculating the data-dependent jitter in broadband systems from the pulse response is discussed. The impact of jitter on conventional clock and data recovery circuits is studied in the time and frequency domain. The deterministic nature of data-dependent jitter suggests equalization techniques suitable for high-speed circuits. Two equalizer circuit implementations are presented. The first is a SiGe clock and data recovery circuit modified to incorporate a deterministic jitter equalizer. This circuit demonstrates the reduction of jitter in the recovered clock. The second circuit is a MOS implementation of a jitter equalizer with independent control of the rising and falling edge timing. This equalizer demonstrates improvement of the timing margins that achieve 10/sup -12/ BER from 30 to 52 ps at 10 Gb/s

Optics for AI and AI for Optics

Artificial intelligence is deeply involved in our daily lives via reinforcing the digital transformation of modern economies and infrastructure. It relies on powerful computing clusters, which face bottlenecks of power consumption for both data transmission and intensive computing. Meanwhile, optics (especially optical communications, which underpin today’s telecommunications) is penetrating short-reach connections down to the chip level, thus meeting with AI technology and creating numerous opportunities. This book is about the marriage of optics and AI and how each part can benefit from the other. Optics facilitates on-chip neural networks based on fast optical computing and energy-efficient interconnects and communications. On the other hand, AI enables efficient tools to address the challenges of today’s optical communication networks, which behave in an increasingly complex manner. The book collects contributions from pioneering researchers from both academy and industry to discuss the challenges and solutions in each of the respective fields

Machine Learning-based Predictive Maintenance for Optical Networks

Optical networks provide the backbone of modern telecommunications by connecting the world faster than ever before. However, such networks are susceptible to several failures (e.g., optical fiber cuts, malfunctioning optical devices), which might result in degradation in the network operation, massive data loss, and network disruption. It is challenging to accurately and quickly detect and localize such failures due to the complexity of such networks, the time required to identify the fault and pinpoint it using conventional approaches, and the lack of proactive efficient fault management mechanisms. Therefore, it is highly beneficial to perform fault management in optical communication systems in order to reduce the mean time to repair, to meet service level agreements more easily, and to enhance the network reliability. In this thesis, the aforementioned challenges and needs are tackled by investigating the use of machine learning (ML) techniques for implementing efficient proactive fault detection, diagnosis, and localization schemes for optical communication systems. In particular, the adoption of ML methods for solving the following problems is explored: - Degradation prediction of semiconductor lasers, - Lifetime (mean time to failure) prediction of semiconductor lasers, - Remaining useful life (the length of time a machine is likely to operate before it requires repair or replacement) prediction of semiconductor lasers, - Optical fiber fault detection, localization, characterization, and identification for different optical network architectures, - Anomaly detection in optical fiber monitoring. Such ML approaches outperform the conventionally employed methods for all the investigated use cases by achieving better prediction accuracy and earlier prediction or detection capability