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

In this paper, we investigate a modified split-step Fourier method to enable computationally-efficient digital nonlinearity compensation for a coherently-detected 112 Gb/s polarization multiplexed quadrature phase shifted keying transmission over a 1,600 km link (20×80 km) with no inline compensation. We report up to 80% reduction in required stages to perform nonlinear compensation, in comparison to the conventional back-propagation algorithm. This method takes into account the correlation between adjacent symbols at a given instant using a weighted-average approach to enable practical digital nonlinearity compensation.

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Modified split-step fourier method for compensation of nonlinear fibre impairments

We present the challenges, opportunities and limitations of DSP technology interoperability between different system vendors for next generation metro / regional optical networks. We compare the performance of single vendor versus multi-vendor DSP units when experimentally transmitting 1 Tb/s PM-16QAM over standard single mode fiber.

Towards DSP technology interoperability for next generation metro/regional optical networks

Currently available electrical components put a limit on the maximum achievable symbol rate per channel and order of modulation for coherent optical transmission systems. Hence, future optical transmission systems are expected to utilize superchannel structure for the long-haul transmission of data rates as high as 400 Gb/s and 1Tb/s. In this paper we discuss the trade-offs at transmitter and receiver to achieve long-haul reach for 1 Tb/s superchannel.

Long-haul WDM transmission of 1 Tb/s superchannel

Digital pre-emphasis (DPE) in optical communications has been proposed to counteract transceiver bandwidth limitations, enabling higher symbol rate transmission with low bandwidth opto-electronic components. However, system design trade-offs for diverse regimes of weak and strong pre-emphasis largely remain unexplored.In this paper, we model 32 Gbaud commercial transceivers’ component behaviour to enable DPE-aided 64 Gbaud operation, as opposed to high bandwidth components’ utilization. We systematically investigate the interplay of DPE parameters for multi-component pre-emphasis, and its impact on available transmitter output power and transmission performance. In particular, we consider 64 Gbaud dual polarization quadrature amplitude modulation (DP-x-QAM), x = 4, 16, 64, enabling 200 Gb/s, 400 Gb/s and 600 Gb/s line rates, and show that DPE may offer performance gains of up to ~4.6 dB, compared to no DPE, however with an associated output power loss of up to ~9 dB. Furthermore, we report that at −16 dBm target optical output power, DP-QPSK shows highest net performance loss, compared to maximal performance, followed by DP-16QAM and DP-64QAM.

Interplay of Pulse Shaping and Pre-Emphasis for High Symbol Rate Coherent Transmission Systems

Enclosed herewith is a method for protecting a link in an optical network configured for transmitting digital data employing a predetermined modulation format which comprises a number of symbols in a constellation diagram. A binary address is associated with each symbol. The modulation format allows for a constellation distortion, according to which the relative positions of constellation points in the constellation diagram are varied in a predetermined way by a predetermined degree. The method comprises the steps of: 
A) partitioning the traffic in two or more priority classes, 
B) mapping higher priority traffic to predefined bit positions within the binary symbol addresses, 
C) evaluating the quality of a predetermined protection link, 
D) determining a degree of distortion such that a desired transmission quality for the transmission of the traffic of the highest priority class or classes via said predetermined protection link and a desired transmission quality for the full traffic via said given link are simultaneously ensured, and 
E) employing said distorted constellation diagram for transmission of digital data over said given link.

Danish Rafique

Papers

Modified split-step fourier method for compensation of nonlinear fibre impairments

Towards DSP technology interoperability for next generation metro/regional optical networks

Long-haul WDM transmission of 1 Tb/s superchannel

Interplay of Pulse Shaping and Pre-Emphasis for High Symbol Rate Coherent Transmission Systems

A method for protecting a link in an optical network