Journal of Telecommunications and Information Technology, 2011, nr 4

kwartalni

Deep Learning Methods for Nonlinearity Mitigation in Coherent Fiber-Optic Communication Links

Nowadays, the demand for telecommunication services is rapidly growing. To meet this everincreasing connectivity demand telecommunication industry needs to maintain the exponential growth of capacity supply. One of the central efforts in this initiative is directed towards coherent fiber-optic communication systems, the backbone of modern telecommunication infrastructure. Nonlinear distortions, i.e., the ones dependent on the transmitted signal, are widely considered to be one of the major limiting factors of these systems. When mitigating these distortions, we can’t rely on the pre-recorded information about channel properties, which is often missing or incorrect, and, therefore, have to resort to adaptive mitigation techniques, learning the link properties by themselves. Unfortunately, the existing practical approaches are suboptimal: they assume weak nonlinear distortion and propose its compensation via a cascade of separately trained sub-optimal algorithms. Deep learning, a subclass of machine learning very popular nowadays, proposes a way to address these problems. First, deep learning solutions can approximate well an arbitrary nonlinear function without making any prior assumptions about it. Second, deep learning solutions can effectively optimize a cluster of single-purpose algorithms, which leads them to a global performance optimum. In this thesis, two deep-learning solutions for nonlinearity mitigation in high-baudrate coherent fiber-optic communication links are proposed. The first one is the data augmentation technique for improving the training of supervised-learned algorithms for the compensation of nonlinear distortion. Data augmentation encircles a set of approaches for enhancing the size and the quality of training datasets so that they can lead us to better supervised learned models. This thesis shows that specially designed data augmentation techniques can be a very efficient tool for the development of powerful supervised-learned nonlinearity compensation algorithms. In various testcases studied both numerically and experimentally, the suggested augmentation is shown to lead to the reduction of up to 6× in the size of the dataset required to achieve the desired performance and a nearly 2× reduction in the training complexity of a nonlinearity compensation algorithm. The proposed approach is generic and can be applied to enhance a multitude of supervised-learned nonlinearity compensation techniques. The second one is the end-to-end learning procedure enabling optimization of the joint probabilistic and geometric shaping of symbol sequences. In a general end-to-end learning approach, the whole system is implemented as a single trainable NN from bits-in to bits-out. The novelty of the proposed approach is in using cost-effective channel model based on the perturbation theory and the refined symbol probabilities training procedure. The learned constellation shaping demonstrates a considerable mutual information gains in single-channel 64 GBd transmission through both single-span 170 km and multi-span 30x80 km single-mode fiber links. The suggested end-to-end learning procedure is applicable to an arbitrary coherent fiber-optic communication link

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction

Adaptive optical feedforward linearization of optical transceiver for radio over fiber communication link

With the tremendous growth in numbers of mobile data subscribers and explosive demand for mobile data, the current wireless access network need to be augmented in order to keep up with the data speed promised by the future generation mobile network standards. Radio over fiber technology (RoF) is a cost effective solution because of its ability to support numerous numbers of simple structured base stations by consolidating the signal processing functions at the central station. RoF systems are analog systems where noise figure and spurious free dynamic range (SFDR) are important parameters in an RoF link. The nonlinearity of a laser transmitter is a major limiting factor to the performance of an RoF link, as it generates spurious spectral components, leading to intermodulation distortions (IMD), which limit the achievable SFDR of the analog RF wave transmissions. The device nonlinearity can be mitigated through various linearization schemes. The feedforward linearization technique offers a number of advantages compared to other techniques, as it offers good suppression of distortion products over a large bandwidth and supports high operating frequencies. On the other hand, feedforward linearization is a relatively sensitive scheme, where its performance is highly influenced by changing operating conditions such as laser aging, temperature effect, and input signal variations. Therefore, for practical implementations the feedforward system has to be real-time adaptive. This thesis aims to develop an adaptive optical feedforward linearization system for radio over fiber links. Mathematical analyses and computer simulations are performed to determine the most efficient algorithm for the adaptive controller for laser transmitter feedforward linearization system. Experimental setup and practical measurement are performed for an adaptive feedforward linearized laser transmitter and its performance is optimized. The adaptive optical feedforward linearization system has been modeled and simulated in MATLAB Simulink. The performances of two adaptive algorithms, which are related to the gradient signal method, such as least mean square (LMS) and recursive least square (RLS) have been compared. The LMS algorithm has been selected because of its robustness and simplicity. Finally, the adaptive optical feedforward linearization system has been set up with digital signal processor (DSP) as the control device, and practical measurement has been performed. The system has achieved a suppression of 14 dB in the third order IMD products over a bandwidth of 30 MHz, in a two-tone measurement at 1.7 GHz

Visible Light Communication (VLC)

Visible light communication (VLC) using light-emitting diodes (LEDs) or laser diodes (LDs) has been envisioned as one of the key enabling technologies for 6G and Internet of Things (IoT) systems, owing to its appealing advantages, including abundant and unregulated spectrum resources, no electromagnetic interference (EMI) radiation and high security. However, despite its many advantages, VLC faces several technical challenges, such as the limited bandwidth and severe nonlinearity of opto-electronic devices, link blockage and user mobility. Therefore, significant efforts are needed from the global VLC community to develop VLC technology further. This Special Issue, “Visible Light Communication (VLC)”, provides an opportunity for global researchers to share their new ideas and cutting-edge techniques to address the above-mentioned challenges. The 16 papers published in this Special Issue represent the fascinating progress of VLC in various contexts, including general indoor and underwater scenarios, and the emerging application of machine learning/artificial intelligence (ML/AI) techniques in VLC

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

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

SMARAD - Centre of Excellence in Smart Radios and Wireless Research - Activity Report 2011 - 2013

Centre of Excellence in Smart Radios and Wireless Research (SMARAD), originally established with the name Smart and Novel Radios Research Unit, is aiming at world-class research and education in Future radio and antenna systems, Cognitive radio, Millimetre wave and THz techniques, Sensors, and Materials and energy, using its expertise in RF, microwave and millimeter wave engineering, in integrated circuit design for multi-standard radios as well as in wireless communications. SMARAD has the Centre of Excellence in Research status from the Academy of Finland since 2002 (2002-2007 and 2008-2013). Currently SMARAD consists of five research groups from three departments, namely the Department of Radio Science and Engineering, Department of Micro and Nanosciences, and Department of Signal Processing and Acoustics, all within the Aalto University School of Electrical Engineering. The total number of employees within the research unit is about 100 including 8 professors, about 30 senior scientists and about 40 graduate students and several undergraduate students working on their Master thesis. The relevance of SMARAD to the Finnish society is very high considering the high national income from exports of telecommunications and electronics products. The unit conducts basic research but at the same time maintains close co-operation with industry. Novel ideas are applied in design of new communication circuits and platforms, transmission techniques and antenna structures. SMARAD has a well-established network of co-operating partners in industry, research institutes and academia worldwide. It coordinates a few EU projects. The funding sources of SMARAD are diverse including the Academy of Finland, EU, ESA, Tekes, and Finnish and foreign telecommunications and semiconductor industry. As a by-product of this research SMARAD provides highest-level education and supervision to graduate students in the areas of radio engineering, circuit design and communications through Aalto University and Finnish graduate schools. During years 2011 – 2013, 18 doctor degrees were awarded to the students of SMARAD. In the same period, the SMARAD researchers published 197 refereed journal articles and 360 conference papers

SMARAD - Centre of Excellence in Smart Radios and Wireless Research - Activity Report 2008 - 2010

Centre of Excellence in Smart Radios and Wireless Research (SMARAD), originally established with the name Smart and Novel Radios Research Unit, is aiming at world-class research and education in Future radio and antenna systems, Cognitive radio, Millimetre wave and THz techniques, Sensors, and Materials and energy, using its expertise in RF, microwave and millimetre wave engineering, in integrated circuit design for multi-standard radios as well as in wireless communications. SMARAD has the Centre of Excellence in Research status from the Academy of Finland since 2002 (2002-2007 and 2008-2013). Currently SMARAD consists of five research groups from three departments, namely the Department of Radio Science and Engineering, Department of Micro and Nanosciences, and Department of Signal Processing and Acoustics, all within the Aalto University School of Electrical Engineering. The total number of employees within the research unit is about 100 including 8 professors, about 30 senior scientists and about 40 graduate students and several undergraduate students working on their Master thesis. The relevance of SMARAD to the Finnish society is very high considering the high national income from exports of telecommunications and electronics products. The unit conducts basic research but at the same time maintains close co-operation with industry. Novel ideas are applied in design of new communication circuits and platforms, transmission techniques and antenna structures. SMARAD has a well-established network of co-operating partners in industry, research institutes and academia worldwide. It coordinates a few EU projects. The funding sources of SMARAD are diverse including the Academy of Finland, EU, ESA, Tekes, and Finnish and foreign telecommunications and semiconductor industry. As a byproduct of this research SMARAD provides highest-level education and supervision to graduate students in the areas of radio engineering, circuit design and communications through Aalto University and Finnish graduate schools such as Graduate School in Electronics, Telecommunications and Automation (GETA). During years 2008 – 2010, 21 doctor degrees were awarded to the students of SMARAD. In the same period, the SMARAD researchers published 141 refereed journal articles and 333 conference papers