3,884 research outputs found
Time-Frequency Packing for High Capacity Coherent Optical Links
We consider realistic long-haul optical links, with linear and nonlinear
impairments, and investigate the application of time-frequency packing with
low-order constellations as a possible solution to increase the spectral
efficiency. A detailed comparison with available techniques from the literature
will be also performed. We will see that this technique represents a feasible
solution to overcome the relevant theoretical and technological issues related
to this spectral efficiency increase and could be more effective than the
simple adoption of high-order modulation formats.Comment: 10 pages, 9 figures. arXiv admin note: text overlap with
arXiv:1406.5685 by other author
Demodulation and Detection Schemes for a Memoryless Optical WDM Channel
It is well known that matched filtering and sampling (MFS) demodulation
together with minimum Euclidean distance (MD) detection constitute the optimal
receiver for the additive white Gaussian noise channel. However, for a general
nonlinear transmission medium, MFS does not provide sufficient statistics, and
therefore is suboptimal. Nonetheless, this receiver is widely used in optical
systems, where the Kerr nonlinearity is the dominant impairment at high powers.
In this paper, we consider a suite of receivers for a two-user channel subject
to a type of nonlinear interference that occurs in
wavelength-division-multiplexed channels. The asymptotes of the symbol error
rate (SER) of the considered receivers at high powers are derived or bounded
analytically. Moreover, Monte-Carlo simulations are conducted to evaluate the
SER for all the receivers. Our results show that receivers that are based on
MFS cannot achieve arbitrary low SERs, whereas the SER goes to zero as the
power grows for the optimal receiver. Furthermore, we devise a heuristic
demodulator, which together with the MD detector yields a receiver that is
simpler than the optimal one and can achieve arbitrary low SERs. The SER
performance of the proposed receivers is also evaluated for some single-span
fiber-optical channels via split-step Fourier simulations
A novel MLSD receiver architecture for nonlinear channels
A new architecture for maximum likelihood sequence detec- tion (MLSD) in nonlinear dispersive channels (NLCs) is presented, and its robustness to inaccurate channel knowledge is analyzed. This architecture is developed by considering a novel orthogonal representation of the NLC, which is exploited to develop a front-end capable of obtaining uncorrelated symbol rate samples, representing a sufficient statistic for information decoding. This front-end is a special form of space-time whitened matched filter (ST-WMF), and the MLSD obtained by using this front-end (ST-WMF-MLSD) requires simple branch metrics due to the signal whitening. The ST-WMF also allows for space-time compression of the equivalent channel, which is exploited for further complexity reduction of the ST-WMF-MLSD. Simulation results show the good trade-off in performance and complexity obtained with the ST-WMF- MLSD, even in the presence of inaccurate channel knowledge.Sociedad Argentina de Informática e Investigación Operativa (SADIO
A novel MLSD receiver architecture for nonlinear channels
A new architecture for maximum likelihood sequence detec- tion (MLSD) in nonlinear dispersive channels (NLCs) is presented, and its robustness to inaccurate channel knowledge is analyzed. This architecture is developed by considering a novel orthogonal representation of the NLC, which is exploited to develop a front-end capable of obtaining uncorrelated symbol rate samples, representing a sufficient statistic for information decoding. This front-end is a special form of space-time whitened matched filter (ST-WMF), and the MLSD obtained by using this front-end (ST-WMF-MLSD) requires simple branch metrics due to the signal whitening. The ST-WMF also allows for space-time compression of the equivalent channel, which is exploited for further complexity reduction of the ST-WMF-MLSD. Simulation results show the good trade-off in performance and complexity obtained with the ST-WMF- MLSD, even in the presence of inaccurate channel knowledge.Sociedad Argentina de Informática e Investigación Operativa (SADIO
On the Impact of Optimal Modulation and FEC Overhead on Future Optical Networks
The potential of optimum selection of modulation and forward error correction
(FEC) overhead (OH) in future transparent nonlinear optical mesh networks is
studied from an information theory perspective. Different network topologies
are studied as well as both ideal soft-decision (SD) and hard-decision (HD) FEC
based on demap-and-decode (bit-wise) receivers. When compared to the de-facto
QPSK with 7% OH, our results show large gains in network throughput. When
compared to SD-FEC, HD-FEC is shown to cause network throughput losses of 12%,
15%, and 20% for a country, continental, and global network topology,
respectively. Furthermore, it is shown that most of the theoretically possible
gains can be achieved by using one modulation format and only two OHs. This is
in contrast to the infinite number of OHs required in the ideal case. The
obtained optimal OHs are between 5% and 80%, which highlights the potential
advantage of using FEC with high OHs.Comment: Some minor typos were correcte
Remotely pumped optical distribution networks: a distributed amplifier model
Optical distribution networks using remotely pumped erbium-doped fiber amplifiers (EDFAs) with a single pump source at the head end can conveniently provide signal gain without adding to the power-consumption cost and management complexity of having multiple locally pumped EDFAs in densely populated metropolitan areas. We introduce an analytical model for understanding the basic physical foundations of remotely pumped network design and for analyzing the number of users that can be supported using such a remote-pumping scheme
A survey on fiber nonlinearity compensation for 400 Gbps and beyond optical communication systems
Optical communication systems represent the backbone of modern communication
networks. Since their deployment, different fiber technologies have been used
to deal with optical fiber impairments such as dispersion-shifted fibers and
dispersion-compensation fibers. In recent years, thanks to the introduction of
coherent detection based systems, fiber impairments can be mitigated using
digital signal processing (DSP) algorithms. Coherent systems are used in the
current 100 Gbps wavelength-division multiplexing (WDM) standard technology.
They allow the increase of spectral efficiency by using multi-level modulation
formats, and are combined with DSP techniques to combat the linear fiber
distortions. In addition to linear impairments, the next generation 400 Gbps/1
Tbps WDM systems are also more affected by the fiber nonlinearity due to the
Kerr effect. At high input power, the fiber nonlinear effects become more
important and their compensation is required to improve the transmission
performance. Several approaches have been proposed to deal with the fiber
nonlinearity. In this paper, after a brief description of the Kerr-induced
nonlinear effects, a survey on the fiber nonlinearity compensation (NLC)
techniques is provided. We focus on the well-known NLC techniques and discuss
their performance, as well as their implementation and complexity. An extension
of the inter-subcarrier nonlinear interference canceler approach is also
proposed. A performance evaluation of the well-known NLC techniques and the
proposed approach is provided in the context of Nyquist and super-Nyquist
superchannel systems.Comment: Accepted in the IEEE Communications Surveys and Tutorial
Neural networks for optical channel equalization in high speed communication systems
La demande future de bande passante pour les données dépassera les capacités des systèmes de communication optique actuels, qui approchent de leurs limites en raison des limitations de la bande passante électrique des composants de l’émetteur. L’interférence intersymbole (ISI) due à cette limitation de bande est le principal facteur de dégradation pour atteindre des débits de données élevés. Dans ce mémoire, nous étudions plusieurs techniques de réseaux neuronaux (NN) pour combattre les limites physiques des composants de l’émetteur pilotés à des débits de données élevés et exploitant les formats de modulation avancés avec une détection cohérente. Notre objectif principal avec les NN comme égaliseurs de canaux ISI est de surmonter les limites des récepteurs optimaux conventionnels, en fournissant une complexité évolutive moindre et une solution quasi optimale. Nous proposons une nouvelle architecture bidirectionnelle profonde de mémoire à long terme (BiLSTM), qui est efficace pour atténuer les graves problèmes d’ISI causés par les composants à bande limitée. Pour la première fois, nous démontrons par simulation que notre BiLSTM profonde proposée atteint le même taux d’erreur sur les bits(TEB) qu’un estimateur de séquence à maximum de vraisemblance (MLSE) optimal pour la modulation MDPQ. Les NN étant des modèles pilotés par les données, leurs performances dépendent fortement de la qualité des données d’entrée. Nous démontrons comment les performances du BiLSTM profond réalisable se dégradent avec l’augmentation de l’ordre de modulation. Nous examinons également l’impact de la sévérité de l’ISI et de la longueur de la mémoire du canal sur les performances de la BiLSTM profonde. Nous étudions les performances de divers canaux synthétiques à bande limitée ainsi qu’un canal optique mesuré à 100 Gbaud en utilisant un modulateur photonique au silicium (SiP) de 35 GHz. La gravité ISI de ces canaux est quantifiée grâce à une nouvelle vue graphique des performances basée sur les écarts de performance de base entre les solutions optimales linéaires et non linéaires classiques. Aux ordres QAM supérieurs à la QPSK, nous quantifions l’écart de performance BiLSTM profond par rapport à la MLSE optimale à mesure que la sévérité ISI augmente. Alors qu’elle s’approche des performances optimales de la MLSE à 8QAM et 16QAM avec une pénalité, elle est capable de dépasser largement la solution optimale linéaire à 32QAM. Plus important encore, l’avantage de l’utilisation de modèles d’auto-apprentissage comme les NN est leur capacité à apprendre le canal pendant la formation, alors que la MLSE optimale nécessite des informations précises sur l’état du canal.The future demand for the data bandwidth will surpass the capabilities of current optical communication systems, which are approaching their limits due to the electrical bandwidth limitations of the transmitter components. Inter-symbol interference (ISI) due to this band limitation is the major degradation factor to achieve high data rates. In this thesis, we investigate several neural network (NN) techniques to combat the physical limits of the transmitter components driven at high data rates and exploiting the advanced modulation formats with coherent detection. Our main focus with NNs as ISI channel equalizers is to overcome the limitations of conventional optimal receivers, by providing lower scalable complexity and near optimal solution. We propose a novel deep bidirectional long short-term memory (BiLSTM) architecture, that is effective in mitigating severe ISI caused by bandlimited components. For the first time, we demonstrate via simulation that our proposed deep BiLSTM achieves the same bit error rate (BER) performance as an optimal maximum likelihood sequence estimator (MLSE) for QPSK modulation. The NNs being data-driven models, their performance acutely depends on input data quality. We demonstrate how the achievable deep BiLSTM performance degrades with the increase in modulation order. We also examine the impact of ISI severity and channel memory length on deep BiLSTM performance. We investigate the performances of various synthetic band-limited channels along with a measured optical channel at 100 Gbaud using a 35 GHz silicon photonic(SiP) modulator. The ISI severity of these channels is quantified with a new graphical view of performance based on the baseline performance gaps between conventional linear and nonlinear optimal solutions. At QAM orders above QPSK, we quantify deep BiLSTM performance deviation from the optimal MLSE as ISI severity increases. While deep BiLSTM approaches the optimal MLSE performance at 8QAM and 16QAM with a penalty, it is able to greatly surpass the linear optimal solution at 32QAM. More importantly, the advantage of using self learning models like NNs is their ability to learn the channel during the training, while the optimal MLSE requires accurate channel state information
Replacing the Soft FEC Limit Paradigm in the Design of Optical Communication Systems
The FEC limit paradigm is the prevalent practice for designing optical
communication systems to attain a certain bit-error rate (BER) without forward
error correction (FEC). This practice assumes that there is an FEC code that
will reduce the BER after decoding to the desired level. In this paper, we
challenge this practice and show that the concept of a channel-independent FEC
limit is invalid for soft-decision bit-wise decoding. It is shown that for low
code rates and high order modulation formats, the use of the soft FEC limit
paradigm can underestimate the spectral efficiencies by up to 20%. A better
predictor for the BER after decoding is the generalized mutual information,
which is shown to give consistent post-FEC BER predictions across different
channel conditions and modulation formats. Extensive optical full-field
simulations and experiments are carried out in both the linear and nonlinear
transmission regimes to confirm the theoretical analysis
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