60 research outputs found
Coherent Optical OFDM Modem Employing Artificial Neural Networks for Dispersion and Nonlinearity Compensation in a Long-Haul Transmission System
In order to satisfy the ever increasing demand for the bandwidth requirement in broadband services the optical orthogonal frequency division multiplexing (OOFDM) scheme is being considered as a promising technique for future high-capacity optical networks. The aim of this thesis is to investigate, theoretically, the feasibility of implementing the coherent optical OFDM (CO-OOFDM) technique in long haul transmission networks. For CO-OOFDM and Fast-OFDM systems a set of modulation formats dependent analogue to digital converter (ADC) clipping ratio and the quantization bit have been identified, moreover, CO-OOFDM is more resilient to the chromatic dispersion (CD) when compared to the bandwidth efficient Fast-OFDM scheme. For CO-OOFDM systems numerical simulations are undertaken to investigate the effect of the number of sub-carriers, the cyclic prefix (CP), and ADC associated parameters such as the sampling speed, the clipping ratio, and the quantisation bit on the system performance over single mode fibre (SMF) links for data rates up to 80 Gb/s. The use of a large number of sub-carriers is more effective in combating the fibre CD compared to employing a long CP. Moreover, in the presence of fibre non-linearities identifying the optimum number of sub-carriers is a crucial factor in determining the modem performance. For a range of signal data rates up to 40 Gb/s, a set of data rate and transmission distance-dependent optimum ADC parameters are identified in this work. These parameters give rise to a negligible clipping and quantisation noise, moreover, ADC sampling speed can increase the dispersion tolerance while transmitting over SMF links. In addition, simulation results show that the use of adaptive modulation schemes improves the spectrum usage efficiency, thus resulting in higher tolerance to the CD when compared to the case where identical modulation formats are adopted across all sub-carriers. For a given transmission distance utilizing an artificial neural networks (ANN) equalizer improves the system bit error rate (BER) performance by a factor of 50% and 70%, respectively when considering SMF firstly CD and secondly nonlinear effects with CD. Moreover, for a fixed BER of 10-3 utilizing ANN increases the transmission distance by 1.87 times and 2 times, respectively while considering SMF CD and nonlinear effects. The proposed ANN equalizer performs more efficiently in combating SMF non-linearities than the previously published Kerr nonlinearity electrical compensation technique by a factor of 7
Novel linear and nonlinear optical signal processing for ultra-high bandwidth communications
The thesis is articulated around the theme of ultra-wide bandwidth single channel signals. It focuses on the two main topics of transmission and processing of information by techniques compatible with high baudrates. The processing schemes introduced combine new linear and nonlinear optical platforms such as Fourier-domain programmable optical processors and chalcogenide chip waveguides, as well as the concept of neural network. Transmission of data is considered in the context of medium distance links of Optical Time Division Multiplexed (OTDM) data subject to environmental fluctuations. We experimentally demonstrate simultaneous compensation of differential group delay and multiple orders of dispersion at symbol rates of 640 Gbaud and 1.28 Tbaud. Signal processing at high bandwidth is envisaged both in the case of elementary post-transmission analog error mitigation and in the broader field of optical computing for high level operations (“optical processor”). A key innovation is the introduction of a novel four-wave mixing scheme implementing a dot-product operation between wavelength multiplexed channels. In particular, it is demonstrated for low-latency hash-key based all-optical error detection in links encoded with advanced modulation formats. Finally, the work presents groundbreaking concepts for compact implementation of an optical neural network as a programmable multi-purpose processor. The experimental architecture can implement neural networks with several nodes on a single optical nonlinear transfer function implementing functions such as analog-to-digital conversion. The particularity of the thesis is the new approaches to optical signal processing that potentially enable high level operations using simple optical hardware and limited cascading of components
Digital Signal Processing for Optical Communications and Coherent LiDAR
Internet data traffic within data centre, access and metro networks is experiencing
unprecedented growth driven by many data-intensive applications. Significant
efforts have been devoted to the design and implementation of low-complexity
digital signal processing (DSP) algorithms that are suitable for these short-reach
optical links. In this thesis, a novel low-complexity frequency-domain (FD)
multiple-input multiple-output (MIMO) equaliser with momentum-based gradient
descent algorithm is proposed, capable of mitigating both static and dynamic
impairments arising from the optical fibre. The proposed frequency-domain
equaliser (FDE) also improves the robustness of the adaptive equaliser against
feedback latencies which is the main disadvantage of FD adaptive equalisers under
rapid channel variations.
The development and maturity of optical fibre communication techniques over
the past few decades have also been beneficial to many other fields, especially
coherent light detection and ranging (LiDAR) techniques. Many applications
of coherent LiDAR are also cost-sensitive, e.g., autonomous vehicles (AVs).
Therefore, in this thesis, a low-cost and low-complexity single-photodiode-based
coherent LiDAR system is investigated. The receiver sensitivity performance of this
receiver architecture is assessed through both simulations and experiments, using
two ranging waveforms known as double-sideband (DSB) amplitude-modulated
chirp signal and single-sideband (SSB) frequency-modulated continuous-wave
(FMCW) signals. Besides, the impact of laser phase noise on the ranging precision
when operating within and beyond the laser coherence length is studied. Achievable
ranging precision beyond the laser coherence length is quantified
Caracterização de moduladores RSOA em ligações de radio sobre fibra
Doutoramento em Engenharia ElectrotécnicaIn this work physical and behavioral models for a bulk Reflective Semiconductor
Optical Amplifier (RSOA) modulator in Radio over Fiber (RoF) links
are proposed. The transmission performance of the RSOA modulator is predicted
under broadband signal drive.
At first, the simplified physical model for the RSOA modulator in RoF links
is proposed, which is based on the rate equation and traveling-wave equations
with several assumptions. The model is implemented with the Symbolically
Defined Devices (SDD) in Advanced Design System (ADS) and validated
with experimental results. Detailed analysis regarding optical gain,
harmonic and intermodulation distortions, and transmission performance is
performed. The distribution of the carrier and Amplified Spontaneous Emission
(ASE) is also demonstrated.
Behavioral modeling of the RSOA modulator is to enable us to investigate
the nonlinear distortion of the RSOA modulator from another perspective
in system level. The Amplitude-to-Amplitude Conversion (AM-AM) and
Amplitude-to-Phase Conversion (AM-PM) distortions of the RSOA modulator
are demonstrated based on an Artificial Neural Network (ANN) and
a generalized polynomial model. Another behavioral model based on Xparameters
was obtained from the physical model.
Compensation of the nonlinearity of the RSOA modulator is carried out
based on a memory polynomial model. The nonlinear distortion of the RSOA
modulator is reduced successfully. The improvement of the 3rd order intermodulation
distortion is up to 17 dB. The Error Vector Magnitude (EVM) is
improved from 6.1% to 2.0%.
In the last part of this work, the performance of Fibre Optic Networks for
Distributed and Extendible Heterogeneous Radio Architectures and Service
Provisioning (FUTON) systems, which is the four-channel virtual Multiple
Input Multiple Output (MIMO), is predicted by using the developed physical
model. Based on Subcarrier Multiplexing (SCM) techniques, four-channel
signals with 100 MHz bandwidth per channel are generated and used to drive
the RSOA modulator. The transmission performance of the RSOA modulator
under the broadband multi channels is depicted with the figure of merit, EVM
under di erent adrature Amplitude Modulation (QAM) level of 64 and 254
for various number of Orthogonal Frequency Division Multiplexing (OFDM)
subcarriers of 64, 512, 1024 and 2048.Nesta tese são propostos modelos físicos e comportamentais para o amplificador
óptico semicondutor reflectivo (RSOA), tendo como objectivo a avaliação
do seu desempenho quando utilizado como modulador em ligações de
rádio sobre fibra (RoF). Os modelos propostos são capazes de prever o comportamento
do dispositivo quando utilizado com sinais de banda larga bem
como quando estimulado por sinais de elevada potência.
Inicialmente propõe-se um modelo físico simplificado para o RSOA baseado
nas equações de taxa e nas equações de propagação electromagnética. A implementação
do modelo utiliza o ADS (Advanced Design Systems) e o bloco
designado por dispositivo definido simbolicamente (SDD) para descrever as
equações de taxa, assim como a propagação de fotões ao longo da cavidade.
O modelo permite uma análise detalhada do ganho óptico, distorções harmônicas,
intermodulação e seu desempenho de transmissão com portadoras
RF modeladas.
Foram também considerados modelos comportamentais. Um modelo
baseado em rede neural artificial (ANN) e um modelo polinomial generalizado
para banda base foram considerados tendo os parâmetros respectivos
sido extraídos utilizando, para o efeito, dados obtidos experimentalmente.
São demonstradas a característica da distorção resultante da conversão amplitude
- amplitude (AM-AM) e conversão da fase - amplitude (AM-PM) no
modulador RSOA. Um modelo baseado em parametros X, obtidos a partir do
modelo físico, foi também analisado.
Compensação da não-linearidade do modulador RSOA é realizada com base
num modelo polinomial com memória. Demonstra-se que a distorção não
linear do modulador RSOA pode ser compensada com sucesso. Com a compensação
obtem-se uma redução de 17 dB da distorção introduzida pelos
produtos de intermodulação de terceira ordem. O EVM (Error Vector Magnitude)
apresenta uma melhoria de 6,1% para 2,0%.
Na última parte deste trabalho considera-se uma configuração que representa
a ligação ascendente por fibra de um sistema de antenas remoto a
uma estação central de processamento. Com esta configuração pretendese
demonstrar a possibilidade de implementação de uma tecnologia MIMO,
suportada num sistema RoF. Baseado numa técnica de multiplexação subportadora
(SCM), os sinais de quatro canais com largura de banda de 100 MHz
por canal são multiplexados e utilizados para modelar o ganho do RSOA. O
desempenho deste link óptico é caracterizado para modulações OFDM considerando
diferentes números de sub-portadoras por símbolo (64, 512 , 1024
e 2048) assim como o formato QAM imposto sobre cada sub-portadora
Machine Learning For Performance Improvement of Long-Haul End-to-End Optical Transmission Systems
The thesis focuses on addressing the challenges faced by optical fiber networks in keeping up with the growing demand for data transfer, especially with the advent of 5G/6G and the Internet of Things (IoT). The rapid expansion in data transfer requirements highlights the limitations of current optical fiber networks and the necessity for improvements in data encoding techniques, spectrumutilization, and signal clarity over long distances. The thesis contributes to this field by developing new methods for applying the Nonlinear Fourier Transform (NFT) to continuous signals, improving signal processing algorithms, and using Machine learning (ML) to understand complex patterns and make data-driven decisions to optimize optical communication systems. The work is divided into two primary sections. The first section delves into advanced NFT techniques, including their application in optical fiber channel modeling for single and dualpolarization systems, signal processing with a sliding window technique combined with NFT, exploring solitonic components in optical signals, and the use of neural networks for NFT to work with noisy signals. The second section is dedicated to the role of ML in optimizing optical communication systems, discussing the new High-Performance COMmunication library (Hp-Com) framework for simulating optical channels, the use of Gradient Boosting for nonlinear equalization, studying received symbol distributions using the GaussianMixtureModel, and summarizing findings with insights for future research. The thesis outlines the creation of innovative techniques to improve optical fiber systems, thus aiding the continued development of the digital world by handling the ever-increasing demands for data transmission
High mobility in OFDM based wireless communication systems
Orthogonal Frequency Division Multiplexing (OFDM) has been adopted as the transmission scheme in most of the wireless systems we use on a daily basis. It brings with it several inherent advantages that make it an ideal waveform candidate in the physical layer. However, OFDM based wireless systems are severely affected in High Mobility scenarios. In this thesis, we investigate the effects of mobility on OFDM based wireless systems and develop novel techniques to estimate the channel and compensate its effects at the receiver. Compressed Sensing (CS) based channel estimation techniques like the Rake Matching Pursuit (RMP) and the Gradient Rake Matching Pursuit (GRMP) are developed to estimate the channel in a precise, robust and computationally efficient manner. In addition to this, a Cognitive Framework that can detect the mobility in the channel and configure an optimal estimation scheme is also developed and tested. The Cognitive Framework ensures a computationally optimal channel estimation scheme in all channel conditions. We also demonstrate that the proposed schemes can be adapted to other wireless standards easily. Accordingly, evaluation is done for three current broadcast, broadband and cellular standards. The results show the clear benefit of the proposed schemes in enabling high mobility in OFDM based wireless communication systems.Orthogonal Frequency Division Multiplexing (OFDM) wurde als Übertragungsschema in die meisten drahtlosen Systemen, die wir täglich verwenden, übernommen. Es bringt mehrere inhärente Vorteile mit sich, die es zu einem idealen Waveform-Kandidaten in der Bitübertragungsschicht (Physical Layer) machen. Allerdings sind OFDM-basierte drahtlose Systeme in Szenarien mit hoher Mobilität stark beeinträchtigt. In dieser Arbeit untersuchen wir die Auswirkungen der Mobilität auf OFDM-basierte drahtlose Systeme und entwickeln neuartige Techniken, um das Verhalten des Kanals abzuschätzen und seine Auswirkungen am Empfänger zu kompensieren. Auf Compressed Sensing (CS) basierende Kanalschätzverfahren wie das Rake Matching Pursuit (RMP) und das Gradient Rake Matching Pursuit (GRMP) werden entwickelt, um den Kanal präzise, robust und rechnerisch effizient abzuschätzen. Darüber hinaus wird ein Cognitive Framework entwickelt und getestet, das die Mobilität im Kanal erkennt und ein optimales Schätzungsschema konfiguriert. Das Cognitive Framework gewährleistet ein rechnerisch optimales Kanalschätzungsschema für alle möglichen Kanalbedingungen. Wir zeigen außerdem, dass die vorgeschlagenen Schemata auch leicht an andere Funkstandards angepasst werden können. Dementsprechend wird eine Evaluierung für drei aktuelle Rundfunk-, Breitband- und Mobilfunkstandards durchgeführt. Die Ergebnisse zeigen den klaren Vorteil der vorgeschlagenen Schemata bei der Ermöglichung hoher Mobilität in OFDM-basierten drahtlosen Kommunikationssystemen
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