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

Photonic machine learning implementation for signal recovery in optical communications

Machine learning techniques have proven very efficient in assorted classification tasks. Nevertheless, processing time-dependent high-speed signals can turn into an extremely challenging task, especially when these signals have been nonlinearly distorted. Recently, analogue hardware concepts using nonlinear transient responses have been gaining significant interest for fast information processing. Here, we introduce a simplified photonic reservoir computing scheme for data classification of severely distorted optical communication signals after extended fibre transmission. To this end, we convert the direct bit detection process into a pattern recognition problem. Using an experimental implementation of our photonic reservoir computer, we demonstrate an improvement in bit-error-rate by two orders of magnitude, compared to directly classifying the transmitted signal. This improvement corresponds to an extension of the communication range by over 75%. While we do not yet reach full real-time post-processing at telecom rates, we discuss how future designs might close the gap

Receiver Side Signal Processing for Nonlinear Distortion Compensation in 5G AND Beyond

Trading between transmit waveform quality and power efficiency is one of the most challenging issues in radio transmitter implementation. To this end, digital predistortion is the de-facto solution for mitigating power amplifier (PA) nonlinear distortion in cellular base-stations due to its high flexibility and good linearization performance. Theoretically, it is convenient to describe predistorter (PD) transfer function as the mathematical inverse of the PA transfer function, and PD modeling is often performed through parametric methods. Thus, an additional feedback loop is required in the system for PD model parameter estimation. PA is an analog device and DPD is a part of digital front-end, implying that PA output signal is needed to be downconverted to baseband and sampled in the parameter estimation path. Consequently, it is required to employ additional components in the feedback loop such as attenuator, downconverter, and analog-to-digital converter (ADC). In order to be able to capture higher order nonlinearities, it is necessary to perform upsampling operation, which implies that in addition to digital-to-analog converters (DACs) in the forward loop, the components in the feedback loop should support higher bandwidths than the original transmission bandwidth. Additionally, to have a good linearization performance, a high resolution ADC is required. Having an ADC/DAC that supports wide bandwidth and has high resolution is directly increasing the material cost and power consumption. When future millimeter-wave (mmWave) systems are considered, adopting DPD becomes even more complex and costly due to wider waveform bandwidths and employing active antenna arrays. Alternative to DPD, receiver based approaches, referred to as digital post-distortion (DPoD), can be utilized to mitigate the nonlinear effects of transmitter PA. Naturally, receiver side techniques do not provide any improvement in terms of out-of-band (OOB) emission issues, rather they aim to improve received signal error vector magnitude (EVM). As the radiated power at mmWave is typically EVM limited and OOB emission requirements are relaxed compared to sub-6 GHz band, DPoD can offer means for improved network energy-efficiency. Several iterative DPoD methods are proposed in the literature such as power amplifier nonlinearity cancellation (PANC), and reconstruction of distorted signals (RODS). In this thesis, we present a non-iterative computationally efficient receiver side nonlinearity mitigation technique, referred to as digital post-inverse (DPoI), along with the parameter estimation approach targeting existing 5G NR standard-compliant reference signal. The receiver EVM performance of presented approach is analyzed by using computer simulations. It is seen that DPoI can reach similar or improved performance compared to the iterative PANC method, which is chosen as a reference DPoD method. Moreover, it is shown that both DPoD methods overperform ideally linearized transmitter PA under strong nonlinear conditions, which allows higher power efficiency when receiver side techniques are employed

Wireless Channel Equalization in Digital Communication Systems

Our modern society has transformed to an information-demanding system, seeking voice, video, and data in quantities that could not be imagined even a decade ago. The mobility of communicators has added more challenges. One of the new challenges is to conceive highly reliable and fast communication system unaffected by the problems caused in the multipath fading wireless channels. Our quest is to remove one of the obstacles in the way of achieving ultimately fast and reliable wireless digital communication, namely Inter-Symbol Interference (ISI), the intensity of which makes the channel noise inconsequential. The theoretical background for wireless channels modeling and adaptive signal processing are covered in first two chapters of dissertation. The approach of this thesis is not based on one methodology but several algorithms and configurations that are proposed and examined to fight the ISI problem. There are two main categories of channel equalization techniques, supervised (training) and blind unsupervised (blind) modes. We have studied the application of a new and specially modified neural network requiring very short training period for the proper channel equalization in supervised mode. The promising performance in the graphs for this network is presented in chapter 4. For blind modes two distinctive methodologies are presented and studied. Chapter 3 covers the concept of multiple cooperative algorithms for the cases of two and three cooperative algorithms. The select absolutely larger equalized signal and majority vote methods have been used in 2-and 3-algoirithm systems respectively. Many of the demonstrated results are encouraging for further research. Chapter 5 involves the application of general concept of simulated annealing in blind mode equalization. A limited strategy of constant annealing noise is experimented for testing the simple algorithms used in multiple systems. Convergence to local stationary points of the cost function in parameter space is clearly demonstrated and that justifies the use of additional noise. The capability of the adding the random noise to release the algorithm from the local traps is established in several cases

Simultaneous Wireless Information and Power Transfer in 5G communication

Green communication technology is expected to be widely adopted in future generation networks to improve energy efficiency and reliability of wireless communication network. Among the green communication technologies,simultaneous wireless information and power transfer (SWIPT) is adopted for its flexible energy harvesting technology through the radio frequency (RF) signa lthati sused for information transmission. Even though existing SWIPT techniques are flexible and adoptable for the wireless communication networks, the power and time resources of the signal need to be shared between infor- mation transmission and RF energy harvesting, and this compromises the quality of the signal. Therefore,SWIP Ttechniques need to be designed to allow an efficient resource allocation for communication and energy harvesting. The goal oft his thesisis to design SWIP Ttechniques that allow efficient,reliable and secure joint communications and power transference. A problem associated to SWIPT techniques combined with multi carrier signals is that the increased power requirements inherent to energy harvesting purposes can exacerbate nonlinear distortion effects at the transmitter. Therefore, we evaluate nonlinear distortion and present feasible solutions to mitigate the impact of nonlinear distortion effects on the performance.Another goal of the thesisis to take advantage of the energy harvesting signals in SWIP Ttechniques for channel estimation and security purposes.Theperformance of these SWIPT techniques is evaluated analytically, and those results are validated by simulations. It is shownthatthe proposed SWIPT schemes can have excellent performance, out performing conventional SWIPT schemes.Espera-se que aschamadas tecnologiasde green communications sejam amplamente ado- tadas em futuras redes de comunicação sem fios para melhorar a sua eficiência energética a fiabilidade.Entre estas,encontram-se as tecnologias SWIPT (Simultaneous Wireless Information and Power Transference), nas quais um sinal radio é usado para transferir simultaneamente potência e informações.Embora as técnicas SWIPT existentes sejam fle- xíveis e adequadas para as redes de comunicações sem fios, os recursos de energia e tempo do sinal precisam ser compartilhados entre a transmissão de informações e de energia, o que pode comprometer a qualidade do sinal. Deste modo,as técnicas SWIPT precisam ser projetadas para permitir uma alocação eficiente de recursos para comunicação e recolha de energia. O objetivo desta tese é desenvolver técnicas SWIPT que permitam transferência de energia e comunicações eficientes,fiáveis e seguras.Um problema associado às técnicas SWIPT combinadas com sinais multi-portadora são as dificuldades de amplificação ine- rentes à combinação de sinais de transmissão de energia com sinais de transferência de dados, que podem exacerbar os efeitos de distorção não-linear nos sinais transmitidos. Deste modo, um dos objectivos desta tese é avaliar o impacto da distorção não-linear em sinais SWIPT, e apresentar soluções viáveis para mitigar os efeitos da distorção não-linear no desempenho da transmissão de dados.Outro objetivo da tese é aproveitar as vantagens dos sinais de transferência de energia em técnicas SWIPT para efeitos de estimação de canal e segurança na comunicação.Os desempenhos dessas técnicas SWIPT são avaliados analiticamente,sendo os respectivos resultados validados por simulações.É mostrado que os esquemas SWIPT propostos podem ter excelente desempenho, superando esquemas SWIPT convencionais

Key Signal Processing Technologies for High-speed Passive Optical Networks

With emerging technologies such as high-definition video, virtual reality, and cloud computing, bandwidth demand in the access networks is ever-increasing. Passive optical network (PON) has become a promising architecture thanks to its low cost and easy management. IEEE and ITU-T standard organizations have been standardizing the next-generation PON, targeting on increasing the single-channel capacity from 10 Gb/s to 25, 50, and 100 Gb/s as the solution to address the dramatic increase of bandwidth demand. However, since the access network is extremely cost-sensitive, many research problems imposed in the physical layer of PON need to be addressed in a cost-efficient way, which is the primary focus of this thesis. Utilizing the low-cost 10G optics to build up high-speed PON systems is a promising approach, where signal processing techniques are key of importance. Two categories of signal processing techniques have been extensively investigated, namely optical signal processing (OSP) and digital signal processing (DSP). Dispersion-supported equalization (DSE) as a novel OSP scheme is proposed to achieve bit-rate enhancement from 10 Gb/s to 25 Gb/s based on 10G class of optics. Thanks to the bandwidth improved by DSE, the non-return-zero on-off keying which is the simplest modulation format is able to be adopted in the PON system without complex modulation or DSP. Meanwhile, OSP is also proposed to work together with DSP enabling 50G PON while simplifying the DSP complexity. Using both DSE and simple feed-forward equalizer is able to support 50 Gb/s PAM-4 transmission with 10G optics. For C-band 50 Gb/s transmission, injection locking techniques as another OSP approach is proposed to compress the directly modulated laser chirp and increase system bandwidth in the optical domain where a doubled capacity from 25 Gb/s to 50 Gb/s over 20 km fiber can be built on top of 10G optics. For DSP, we investigated the advantages of neural network (NN) on the mitigation of the time-varying nonlinear semiconductor optical amplifier pattern effect. In order to reduce the expense caused by the high computation complexity of NN, a pre- equalizer is introduced at the central office that allows cost sharing for all connected access users. In order to push the PON system line rate to 100 Gb/s, a joint nonlinear Tomlinson- Harashima precoding-Volterra algorithm is proposed to compensate for both linear and nonlinear distortions where 100 Gb/s PAM-4 transmission over 20 km fiber with 15 GHz system bandwidth can be achieved

Anwendung von maschinellem Lernen in der optischen Nachrichtenübertragungstechnik

Aufgrund des zunehmenden Datenverkehrs wird erwartet, dass die optischen Netze zukünftig mit höheren Systemkapazitäten betrieben werden. Dazu wird bspw. die kohärente Übertragung eingesetzt, bei der das Modulationsformat erhöht werden kann, erforder jedoch ein größeres SNR. Um dies zu erreichen, wird die optische Signalleistung erhöht, wodurch die Datenübertragung durch die nichtlinearen Beeinträchtigungen gestört wird. Der Schwerpunkt dieser Arbeit liegt auf der Entwicklung von Modellen des maschinellen Lernens, die auf diese nichtlineare Signalverschlechterung reagieren. Es wird die Support-Vector-Machine (SVM) implementiert und als klassifizierende Entscheidungsmaschine verwendet. Die Ergebnisse zeigen, dass die SVM eine verbesserte Kompensation sowohl der nichtlinearen Fasereffekte als auch der Verzerrungen der optischen Systemkomponenten ermöglicht. Das Prinzip von EONs bietet eine Technologie zur effizienten Nutzung der verfügbaren Ressourcen, die von der optischen Faser bereitgestellt werden. Ein Schlüsselelement der Technologie ist der bandbreitenvariable Transponder, der bspw. die Anpassung des Modulationsformats oder des Codierungsschemas an die aktuellen Verbindungsbedingungen ermöglicht. Um eine optimale Ressourcenauslastung zu gewährleisten wird der Einsatz von Algorithmen des Reinforcement Learnings untersucht. Die Ergebnisse zeigen, dass der RL-Algorithmus in der Lage ist, sich an unbekannte Link-Bedingungen anzupassen, während vergleichbare heuristische Ansätze wie der genetische Algorithmus für jedes Szenario neu trainiert werden müssen

Optical Transmission Systems based on the Nonlinear Fourier Transformation

Solitons are stable pulse shapes, which propagate linearly and maintain their shape despite the highly nonlinear fiber optical channel. A challenge in the use of these signal pulses in optical data transmission is to multiplex them with high efficiency. One way to multiplex many solitons is the nonlinear Fourier transform (NFT). With the help of the NFT, signal spectra can be calculated which propagate linearly through a nonlinear channel. Thus, in perspective, it is possible to perform linear transmissions even in highly nonlinear regions with high signal power levels. The NFT decomposes a signal into a dispersive and a solitonic part. The dispersive part is similar to spectra of the conventional linear Fourier transform and dominates especially at low signal powers. As soon as the total power of a signal exceeds a certain limit, solitons arise. A disadvantage of solitons generated digitally by the NFT is their complex shape due to, for example, high electrical bandwidths or a poor peak-to-average power ratio. In the course of this work, a scalable system architecture of a photonic integrated circuit based on a silicon chip was designed, which allows to multiplex several simple solitons tightly together to push the complex electrical generation of higher order solitons into the optical domain. This photonic integrated circuit was subsequently designed and fabricated by the Institute of Integrated Photonics at RWTH Aachen University. Using this novel system architecture and additional equalization concepts designed in this work, soliton transmissions with up to four channels could be successfully realized over more than 5000 km with a very high spectral efficiency of 0.5 b/s/Hz in the soliton range