Techniques de codage pour le multiplexage spatial sur les systèmes fibres optiques

Les deux dernières décennies ont connu une croissance exponentielle de la demande pour plus de capacité dans les réseaux optiques. Cette croissance a été principalement causée par le développement d'Internet et le trafic croissant généré par le nombre croissant des utilisateurs. La fibre optique offre plusieurs degrés de liberté pour augmenter la capacité. La fréquence, le temps, la phase, la polarisation ont déjà été utilisés pour satisfaire la demande de bande passante, ainsi le multiplexage spatial (SDM) reste le seul degré de liberté disponible pouvant être utilisé dans les systèmes optiques afin d'augmenter la capacité. Cependant, les interactions entre les différents canaux spatiaux dans le même milieu de propagation est inévitable. Ces interactions, si elles ne sont pas compensées, entraînent des dégradations qui détériorent les performances du système. À cette fin, des recherches intensives sont menées récemment afin de développer un traitement de signal avancé capable de traiter ces détériorations dans les systèmes à multiplexage spatial. Motivés par le rôle potentiel des fibres optiques multimodes (MMF) dans les futurs systèmes SDM, dans cette thèse, nous présentons des solutions de codage modernes pour réduire la diaphonie non-unitaire qui affecte les modes spatiaux dans les fibres multimodes entraînant une dégradation des performances.In a very fast pace, the last two decades have known an exponential growth in the demand for more optical network capacity, this growth was mainly caused by the built-out of the Internet and the growing traffic generated by an increasing number of users. Since frequency, time, phase, polarization have already been used to satisfy the demand for bandwidth, space-division multiplexing (SDM) remains the only available degree of freedom that can be used in optical transmission systems in order to increase the capacity. However, interactions between spatial channels in the same propagation medium is inevitable. These interactions, if not compensated, result in impairments that deteriorate the system performance. For this purpose, intensive research is being carried out in recent years in order to provide advanced signal processing capable to deal with these impairments in spatial multiplexing systems. Motivated by the potential role of multi-mode fibers (MMFs) in future SDM systems, in this thesis, we present modern coding solutions to mitigate the non-unitary crosstalk known as mode-dependent loss (MDL) that affects spatial modes of MMFs resulting in degraded system performance

Techniques de codage pour le multiplexage spatial sur les systèmes fibres optiques

In a very fast pace, the last two decades have known an exponential growth in the demand for more optical network capacity, this growth was mainly caused by the built-out of the Internet and the growing traffic generated by an increasing number of users. Since frequency, time, phase, polarization have already been used to satisfy the demand for bandwidth, space-division multiplexing (SDM) remains the only available degree of freedom that can be used in optical transmission systems in order to increase the capacity. However, interactions between spatial channels in the same propagation medium is inevitable. These interactions, if not compensated, result in impairments that deteriorate the system performance. For this purpose, intensive research is being carried out in recent years in order to provide advanced signal processing capable to deal with these impairments in spatial multiplexing systems. Motivated by the potential role of multi-mode fibers (MMFs) in future SDM systems, in this thesis, we present modern coding solutions to mitigate the non-unitary crosstalk known as mode-dependent loss (MDL) that affects spatial modes of MMFs resulting in degraded system performance.Les deux dernières décennies ont connu une croissance exponentielle de la demande pour plus de capacité dans les réseaux optiques. Cette croissance a été principalement causée par le développement d'Internet et le trafic croissant généré par le nombre croissant des utilisateurs. La fibre optique offre plusieurs degrés de liberté pour augmenter la capacité. La fréquence, le temps, la phase, la polarisation ont déjà été utilisés pour satisfaire la demande de bande passante, ainsi le multiplexage spatial (SDM) reste le seul degré de liberté disponible pouvant être utilisé dans les systèmes optiques afin d'augmenter la capacité. Cependant, les interactions entre les différents canaux spatiaux dans le même milieu de propagation est inévitable. Ces interactions, si elles ne sont pas compensées, entraînent des dégradations qui détériorent les performances du système. À cette fin, des recherches intensives sont menées récemment afin de développer un traitement de signal avancé capable de traiter ces détériorations dans les systèmes à multiplexage spatial. Motivés par le rôle potentiel des fibres optiques multimodes (MMF) dans les futurs systèmes SDM, dans cette thèse, nous présentons des solutions de codage modernes pour réduire la diaphonie non-unitaire qui affecte les modes spatiaux dans les fibres multimodes entraînant une dégradation des performances

Filtered Multicarrier Waveforms Classification: A Deep Learning-Based Approach

International audienceAutomatic signal recognition (ASR) plays an important role in various applications such as dynamic spectrum access and cognitive radio, hence it will be a key enabler for beyond 5G communications. Recently, many research works have been exploring deep learning (DL) based ASR, where it has been shown that simple convolutional neural networks (CNN) can outperform expert features based techniques. However, such works have been primarily focusing on single-carrier signals. With the advent of spectrally efficient filtered multicarrier waveforms, we propose in this paper, to revisit the DL based ASR to account for the variety and complexity of these new transmission schemes. Specifically, we design two types of classification algorithms. The first one relies on the cyclostationarity characteristics of the investigated waveforms combined with a support vector machine (SVM) classifier; while the second one explores the use of a four-layer CNN which performs both features extraction and classification. The proposed approaches do not require any a priori knowledge of the received signal parameters, and their performance is evaluated in a multipath channel through simulations for a signal-to-noise ratio (SNR) ranging from −8 to 20 dB. The simulation results show that, despite cyclostationary characteristics being highly discriminative, the CNN outperforms the cyclostationary based classification especially for short time received signals, and low SNR levels

Spreading Factor assisted LoRa Localization with Deep Reinforcement Learning

Most of the developed localization solutions rely on RSSI fingerprinting. However, in the LoRa networks, due to the spreading factor (SF) in the network setting, traditional fingerprinting may lack representativeness of the radio map, leading to inaccurate position estimates. As such, in this work, we propose a novel LoRa RSSI fingerprinting approach that takes into account the SF. The performance evaluation shows the prominence of our proposed approach since we achieved an improvement in localization accuracy by up to 6.67% compared to the state-of-the-art methods. The evaluation has been done using a fully connected deep neural network (DNN) set as the baseline. To further improve the localization accuracy, we propose a deep reinforcement learning model that captures the ever-growing complexity of LoRa networks and copes with their scalability. The obtained results show an improvement of 48.10% in the localization accuracy compared to the baseline DNN model.Comment: Accepted for publication in IEEE VTC2023-Sprin

D2D Mobile Relaying Meets NOMA—Part II: A Reinforcement Learning Perspective

Structureless communications such as Device-to-Device (D2D) relaying are undeniably of paramount importance to improving the performance of today’s mobile networks. Such a communication paradigm requires a certain level of intelligence at the device level, thereby allowing it to interact with the environment and make proper decisions. However, decentralizing decision-making may induce paradoxical outcomes, resulting in a drop in performance, which sustains the design of self-organizing yet efficient systems. We propose that each device decides either to directly connect to the eNodeB or get access via another device through a D2D link. In the first part of this article, we describe a biform game framework to analyze the proposed self-organized system’s performance, under pure and mixed strategies. We use two reinforcement learning (RL) algorithms, enabling devices to self-organize and learn their pure/mixed equilibrium strategies in a fully distributed fashion. Decentralized RL algorithms are shown to play an important role in allowing devices to be self-organized and reach satisfactory performance with incomplete information or even under uncertainties. We point out through a simulation the importance of D2D relaying and assess how our learning schemes perform under slow/fast channel fading

Energy-Efficient and Secure Load Balancing Technique for SDN-Enabled Fog Computing

The number of client applications on the fog computing layer is increasing due to advancements in the Internet of Things (IoT) paradigm. Fog computing plays a significant role in reducing latency and enhancing resource usage for IoT users’ tasks. Along with its various benefits, fog computing also faces several challenges, including challenges related to resource overloading, security, node placement, scheduling, and energy consumption. In fog computing, load balancing is a difficult challenge due to the increased number of IoT devices and requests, which requires an equal load distribution throughout all available resources. In this study, we proposed a secure and energy-aware fog computing architecture, and we implemented a load-balancing technique to improve the complete utilization of resources with an SDN-enabled fog environment. A deep belief network (DBN)-based intrusion detection method was also implemented as part of the proposed techniques to reduce workload communication delays in the fog layer. The simulation findings showed that the proposed technique provided an efficient method of load balancing in a fog environment, minimizing the average response time, average energy consumption, and communication delay by 15%, 23%, and 10%, respectively, as compared with other existing techniques