Modélisation d'un canal minier Ultra Large Bande(UWB) en utilisant les réseaux de neurones Artificiels RBF

Dans un environnement m1mer, le besoin d'un système de communication fiable est primordial. Ce type d'environnement étant complexe, le déploiement des systèmes de communication nécessite une connaissance parfaite du milieu de propagation. La modélisation d'un canal présente une matière très intéressante dans le domaine de recherche, et elle a été traitée dans plusieurs travaux en se basant sur des modèles traditionnels permettant de déterminer le comportement du canal de propagation selon des modèles analytiques, empiriques ou stochastiques. Dans notre projet d'étude, on s'intéresse à la modélisation du canal ultra-large bande (Ultra-Wide band UWB) dans un environnement minier en se basant sur une méthode différente des méthodes traditionnelles ; l'utilisation des réseaux de neurones artificiels RBF (réseaux à fonction de base radiale). Le canal de transmission UWB est un canal à trajets multiples, surtout au cas des applications à l'intérieur. Dans notre recherche, on s'est basé sur des mesures réalisées par LR TCS dans la mine CANMET à V al d'or située à 500km au nord de Montréal, Canada. Ces mesures ont servi comme base de données pour entrainer et créer l'architecture du réseau de neurones RBF. Dans un travail antérieur, la modélisation du canal en utilisant les réseaux de neurones est réalisée, mais en utilisant les réseaux de neurones multicouches MLP (Multi Layer Perceptron), et en s'intéressant seulement à l'affaiblissement de parcours en visibilité directe (line of sight, LOS). Dans ce travail, nous nous sommes intéressés à estimer l'affaiblissement du parcours1 (Path Joss) sur toute la bande UWB, et la phase du signal reçu, en considérant les deux types de trajets : en visibilité directe (LOS) et en visibilité non directe (non line of sight, NLOS). Les résultats obtenus montrent la capacité de ce type de réseau à modéliser le canal UWB dans un environnement minier avec une haute précision

Novel improvements of empirical wireless channel models and proposals of machine-learning-based path loss prediction models for future communication networks.

Doctoral Degree. University of KwaZulu-Natal, Durban.Path loss is the primary factor that determines the overall coverage of networks. Therefore, designing reliable wireless communication systems requires accurate path loss prediction models. Future wireless mobile systems will rely mainly on the super-high frequency (SHF) and the millimeter-wave (mmWave) frequency bands due to the massively available bandwidths that will meet projected users’ demands, such as the needs of the fifth-generation (5G) wireless systems and other high-speed multimedia services. However, these bands are more sensitive and exhibit a different propagation behavior compared to the frequency bands below 6 GHz. Hence, improving the existing models and developing new models are vital for characterizing the wireless communication channel in both indoor and outdoor environments for future SHF and mmWave services. This dissertation proposes new path loss and LOS probability models and efficiently improves the well-known close-in (CI) free space reference distance model and the floating-intercept (FI) model. Real measured data was taken for both line-of-sight (LOS) and non-line-of-sight (NLOS) communication scenarios in a typical indoor corridor environment at three selected frequencies within the SHF band, namely 14 GHz, 18 GHz, and 22 GHz. The research finding of this work reveals that the proposed models have better performance in terms of their accuracy in fitting real measured data collected from measurement campaigns. In addition, this research studies the impact of the angle of arrival and the antenna heights on the current and improved CI and FI models. The results show that the proposed improved models provide better stability and sensitivity to the change of these parameters. Furthermore, the mean square error between the models and their improved versions was presented as another proof of the superiority of the proposed improvement. Moreover, this research shows that shadow fading’s standard deviation can have a notable reduction in both the LOS and NLOS scenarios (especially in the NLOS), which means higher precision in predicting the path loss compared to the existing standard models. After that, the dissertation presents investigations on high-ordering the dependency of the standard CI path loss model on the distance between the transmitting and the receiving antennas at the logarithmic scale. Two improved models are provided and discussed: second-order CI and third-order CI models. The main results reveal that the proposed two models outperform the standard CI model and notable reductions in the shadow fading’s standard deviation values as the model’s order increases, which means that more precision is provided. This part of the dissertation also provides a trade-off study between the model’s accuracy and simplicity

Performance evaluation of LoRa LPWAN technology for IoT-based blast-induced ground vibration system

The recent proliferation of wireless sensor networks (WSNs) evolution into the Internet of Things (IoT) vision enables a variety of low-cost monitoring applications which allows a seamless transfer of information via embedded computing and network devices. Ambiguous ground vibration can be induced by blasting demolition is a severe concern which grievously damages the nearby dwellings and plants. It is an indispensable prerequisite for measuring the blast-induced ground vibration (BIGV), accomplishing a topical and most active research area. Thus, proposed and developed an architecture which emphasizes the IoT realm and implements a low-power wide-area networks (LPWANs) based system. Especially, using the available Long-Range (LoRa) Correct as Radio Frequency (RF) module, construct a WSN configuration for acquisition and streaming of required data from and to an IoT gateway. The system can wirelessly deliver the information to mine management and surrounding rural peoples to aware of the intensity of BIGV level. In this article, an endeavor has been made to introduce a LoRa WAN connectivity and proved the potentiality of the integrated WSN paradigm by testing of data transmission-reception in a non-line of sight (NLOS) condition. The path loss metrics and other required parameters have been measured using the LoRa WAN technology at 2.4 GHz frequency

XR-RF Imaging Enabled by Software-Defined Metasurfaces and Machine Learning: Foundational Vision, Technologies and Challenges

We present a new approach to Extended Reality (XR), denoted as iCOPYWAVES, which seeks to offer naturally low-latency operation and cost-effectiveness, overcoming the critical scalability issues faced by existing solutions. iCOPYWAVES is enabled by emerging PWEs, a recently proposed technology in wireless communications. Empowered by intelligent (meta)surfaces, PWEs transform the wave propagation phenomenon into a software-defined process. We leverage PWEs to i) create, and then ii) selectively copy the scattered RF wavefront of an object from one location in space to another, where a machine learning module, accelerated by FPGAs, translates it to visual input for an XR headset using PWEdriven, RF imaging principles (XR-RF). This makes for an XR system whose operation is bounded in the physical layer and, hence, has the prospects for minimal end-to-end latency. Over large distances, RF-to-fiber/fiber-to-RF is employed to provide intermediate connectivity. The paper provides a tutorial on the iCOPYWAVES system architecture and workflow. A proof-of-concept implementation via simulations is provided, demonstrating the reconstruction of challenging objects in iCOPYWAVES produced computer graphics

THz Wireless Channel Characterization and Modeling for Chip-to-Chip Communication in Computing Systems

This research focuses on the characterization and modeling of the THz wireless channel for chip-to-chip communication in computing systems. To understand the signal propagation mechanisms in a metal enclosure, this thesis presents the channel characterizations inside a desktop sized metal cavity with the consideration of several potential scenarios. Based on the measurement findings, a path loss model for THz chip-to-chip communication is proposed. According to the cavity environment and the statistical properties of the channel inside the metal cavity, a geometry based statistical channel model is constructed. Afterwards, a more practical motherboard desktop environment is investigated by putting the densely populated motherboard in the metal cavity. Both channel characterization and modeling are presented in the thesis for this practical environment. Besides that, deep learning methods are applied on the property prediction of THz wireless channel in the motherboard desktop environment. A ResNet based model is proposed and analyzed for the prediction of the scenario the channel is under and the attribute of the predicted scenario. The objective of this research is to provide other researchers with guidelines on how to characterize and model the wireless channel in computing systems, and provide the channel information for future THz chip-to-chip wireless system design.Ph.D

Cooperative Localization in Mines Using Fingerprinting and Neural Networks

This work is a special investigation in the localization of users in underground and confined areas such as gold mines. It sheds light on the basic approaches that are used nowadays to estimate the position and track users using wireless technology. Localization or Geo-location in confined and underground areas is one of the topics under research in mining labs and industries. The position of personnel and equipments in areas such as mines is of high importance because it improves industrial safety and security. Due to the special nature of underground environments, signals transmitted in a mine gallery suffer severe multipath effects caused by reflection, refraction, diffraction and collision with humid rough surfaces. In such cases and in cases where the signals are blocked due to the non-line of sight (NLOS) regions, traditional localization techniques based on the RSS, AOA and TOA/TDOA lead to high position estimation errors. One of the proposed solutions to such challenging situations is based on extracting the channel impulse response fingerprints with reference to one wireless receiver and using an artificial neural network as the matching algorithm. In this work we study this approach in a multiple access network where multiple access points are present. The diversity of the collected fingerprints allows us to create artificial neural networks that work separately or cooperatively using the same localization technique. In this approach, the received signals by the mobile at various distances are analysed and several components of each signal are extracted accordingly. The channel impulse response found at each position is unique to the position of the receiver. The parameters extracted from the CIR are the received signal strength, mean excess delay, root mean square, maximum excess delay, the number of multipath components, the total power of the received signal, the power of the first arrival and the delay of the first arrival. The use of multiple fingerprints from multiple references not only adds diversity to the set of inputs fed to the neural network but it also enhances the overall concept and makes it applicable in a multi-access environment. Localization is analyzed in the presence of two receivers using several position estimation procedures. The results showed that using two CIRs in a cooperative localization technique gives a position accuracy less than or equal to 1m for 90% of both trained and untrained neural networks. Another way of using cooperative intelligence is by using the time domain including tracking, probabilities and previous positions to the localization system. Estimating new positions based on previous positions recorded in history has a great improvement factor on the accuracy of the localization system where it showed an estimation error of less than 50cm for 90% of training data and 65cm for testing data. The details of those techniques and the estimation errors and graphs are fully presented and they show that using cooperative artificial intelligence in the presence of multiple signatures from different reference points as well as using tracking improves significantly the accuracy, precision, scalability and the overall performance of the localization system

Performance Analysis and Comparison of Non-ideal Wireless PBFT and RAFT Consensus Networks in 6G Communications

Due to advantages in security and privacy, blockchain is considered a key enabling technology to support 6G communications. Practical Byzantine Fault Tolerance (PBFT) and RAFT are seen as the most applicable consensus mechanisms (CMs) in blockchain-enabled wireless networks. However, previous studies on PBFT and RAFT rarely consider the channel performance of the physical layer, such as path loss and channel fading, resulting in research results that are far from real networks. Additionally, 6G communications will widely deploy high-frequency signals such as terahertz (THz) and millimeter wave (mmWave), while performances of PBFT and RAFT are still unknown when these signals are transmitted in wireless PBFT or RAFT networks. Therefore, it is urgent to study the performance of non-ideal wireless PBFT and RAFT networks with THz and mmWave signals, to better make PBFT and RAFT play a role in the 6G era. In this paper, we study and compare the performance of THz and mmWave signals in non-ideal wireless PBFT and RAFT networks, considering Rayleigh Fading (RF) and close-in Free Space (FS) reference distance path loss. Performance is evaluated by five metrics: consensus success rate, latency, throughput, reliability gain, and energy consumption. Meanwhile, we find and derive that there is a maximum distance between two nodes that can make CMs inevitably successful, and it is named the active distance of CMs. The research results not only analyze the performance of non-ideal wireless PBFT and RAFT networks, but also provide important references for the future transmission of THz and mmWave signals in PBFT and RAFT networks.Comment: arXiv admin note: substantial text overlap with arXiv:2303.1575

AI and IoT Meet Mobile Machines: Towards a Smart Working Site

Infrastructure construction is society's cornerstone and economics' catalyst. Therefore, improving mobile machinery's efficiency and reducing their cost of use have enormous economic benefits in the vast and growing construction market. In this thesis, I envision a novel concept smart working site to increase productivity through fleet management from multiple aspects and with Artificial Intelligence (AI) and Internet of Things (IoT)