Several approximate non-linear fiber propagation models have been proposed over the years. Recent re-consideration and extension of earlier modeling efforts has led to the formalization of the so-called Gaussian-noise (GN) model. The evidence collected so far hints at the GN-model as being a relatively simple and, at the same time, sufficiently reliable tool for performance prediction of uncompensated coherent systems, characterized by a favorable accuracy versus complexity trade-off. This paper tries to gather the recent results regarding the GN-model definition, understanding, relations versus other models, validation, limitations, closed form solutions, approximations and, in general, its applications and implications in link analysis and optimization, also within a network environment.

The GN-Model of Fiber Non-Linear Propagation and its Applications

6G and beyond will fulfill the requirements of a fully connected world and provide ubiquitous wireless connectivity for all. Transformative solutions are expected to drive the surge for accommodating a rapidly growing number of intelligent devices and services. Major technological breakthroughs to achieve connectivity goals within 6G include: (i) a network operating at the THz band with much wider spectrum resources, (ii) intelligent communication environments that enable a wireless propagation environment with active signal transmission and reception, (iii) pervasive artificial intelligence, (iv) large-scale network automation, (v) an all-spectrum reconfigurable front-end for dynamic spectrum access, (vi) ambient backscatter communications for energy savings, (vii) the Internet of Space Things enabled by CubeSats and UAVs, and (viii) cell-free massive MIMO communication networks. In this roadmap paper, use cases for these enabling techniques as well as recent advancements on related topics are highlighted, and open problems with possible solutions are discussed, followed by a development timeline outlining the worldwide efforts in the realization of 6G. Going beyond 6G, promising early-stage technologies such as the Internet of NanoThings, the Internet of BioNanoThings, and quantum communications, which are expected to have a far-reaching impact on wireless communications, have also been discussed at length in this paper.

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6G and Beyond: The Future of Wireless Communications Systems

Lightwave communications is a necessity for the information age. Optical links provide enormous bandwidth, and the optical fiber is the only medium that can meet the modern society's needs for transporting massive amounts of data over long distances. Applications range from global high-capacity networks, which constitute the backbone of the internet, to the massively parallel interconnects that provide data connectivity inside datacenters and supercomputers. Optical communications is a diverse and rapidly changing field, where experts in photonics, communications, electronics, and signal processing work side by side to meet the ever-increasing demands for higher capacity, lower cost, and lower energy consumption, while adapting the system design to novel services and technologies. Due to the interdisciplinary nature of this rich research field, Journal of Optics has invited 16 researchers, each a world-leading expert in their respective subfields, to contribute a section to this invited review article, summarizing their views on state-of-the-art and future developments in optical communications.

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Roadmap of optical communications

Today’s telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users’ behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, machine learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing, and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude this paper proposing new possible research directions.

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An Overview on Application of Machine Learning Techniques in Optical Networks

In recent years, with the rapid development of current Internet and mobile communication technologies, the infrastructure, devices and resources in networking systems are becoming more complex and heterogeneous. In order to efficiently organize, manage, maintain and optimize networking systems, more intelligence needs to be deployed. However, due to the inherently distributed feature of traditional networks, machine learning techniques are hard to be applied and deployed to control and operate networks. Software defined networking (SDN) brings us new chances to provide intelligence inside the networks. The capabilities of SDN (e.g., logically centralized control, global view of the network, software-based traffic analysis, and dynamic updating of forwarding rules) make it easier to apply machine learning techniques. In this paper, we provide a comprehensive survey on the literature involving machine learning algorithms applied to SDN. First, the related works and background knowledge are introduced. Then, we present an overview of machine learning algorithms. In addition, we review how machine learning algorithms are applied in the realm of SDN, from the perspective of traffic classification, routing optimization, quality of service/quality of experience prediction, resource management and security. Finally, challenges and broader perspectives are discussed.

A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges

Next generation optical transceivers for data center interconnects are expected to feature bandwidths in excess of 400Gb/s, while exhibiting a smaller footprint, lower power consumption and lower cost per Gb/s compared to current generation 100Gb/s transceivers. To this end, new technologies such as photonic hybrid integration are explored within research and industry and standardization efforts are made to ensure cross compatibility between vendors and ultimately to reduce costs. New packaging concepts such as optical BGAs (OpBGA) are needed to increase RF bandwidth between DSP and optical engine and use of standard Surface Mount Technology (SMT) assembly methods as known from the electronics industry allows for a high degree of automation, eliminating the need for time consuming manual assembly processes. Similarly, automated wafer scale testing of electronic-photonic integrated circuits (EPIC) is a critical part in reducing manufacturing time and thus will lead to reduced DCI costs.

Photonic Integrated Circuits for Data Center Interconnects

We report on fiber nonlinearities based traffic allocation strategies, supporting 240Gb/s and 480Gb/s PM-16QAM channel upgrades, and show that, for both high and low dispersion fibers, homogenous and heterogeneous traffic allocation enables comparable transmission performance.

Beyond 100G flex-grid network upgrades: on the traffic allocation strategies

Thanks to coherent detection and digital signal processing (DSP), linear distortions such as chromatic dispersion (CD) and polarisation mode dispersion (PMD) can in principle be completely compensated for in high-speed optical transmission. And indeed, effective algorithms have been devised and extensively investigated that allow CD- and PMD-resilient transmission of high-speed signals over long distances, leaving optical noise accumulation and non-linear impairments as the factors ultimately limiting reach. Considerable research has been dedicated in the last couple of years to devise methods to increase the non-linear tolerance of optical signals by means of digital signal processing. In this review paper, we present an overview of the most promising techniques, show some examples of their application and outline the status of research on this important topic.

Non-linear compensation techniques for coherent fibre transmission

We report the impact of longitudinal signal power profile on the transmission performance of coherently-detected 112 Gb/s m-ary polarization multiplexed quadrature amplitude modulation system after compensation of deterministic nonlinear fibre impairments. Performance improvements up to 0.6 dB (Q(eff)) are reported for a non-uniform transmission link power profile. Further investigation reveals that the evolution of the transmission performance with power profile management is fully consistent with the parametric amplification of the amplified spontaneous emission by the signal through four-wave mixing. In particular, for a non-dispersion managed system, a single-step increment of 4 dB in the amplifier gain, with respect to a uniform gain profile, at similar to 2/3(rd) of the total reach considerably improves the transmission performance for all the formats studied. In contrary a negative-step profile, emulating a failure (gain decrease or loss increase), significantly degrades the bit-error rate.

Impact of longitudinal power budget in coherent transmission systems employing digital back-propagation

We demonstrate 64 GBd signal generation up to bipolar-8-ASK utilizing a single MZM, monolithically integrated with segmented drivers in SiGe. Using polarization multiplexing, 300-Gb/s net data rate transmission over 120 km SSMF is shown.

Danish Rafique

Papers

Photonic Integrated Circuits for Data Center Interconnects

Beyond 100G flex-grid network upgrades: on the traffic allocation strategies

Non-linear compensation techniques for coherent fibre transmission

Impact of longitudinal power budget in coherent transmission systems employing digital back-propagation

64 GBd DP-Bipolar-8ASK Transmission over 120 km SSMF Employing a Monolithically Integrated Driver and MZM in 0.25-um SiGe BiCMOS Technology