1,202 research outputs found

    Reliable indoor optical wireless communication in the presence of fixed and random blockers

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    The advanced innovation of smartphones has led to the exponential growth of internet users which is expected to reach 71% of the global population by the end of 2027. This in turn has given rise to the demand for wireless data and internet devices that is capable of providing energy-efficient, reliable data transmission and high-speed wireless data services. Light-fidelity (LiFi), known as one of the optical wireless communication (OWC) technology is envisioned as a promising solution to accommodate these demands. However, the indoor LiFi channel is highly environment-dependent which can be influenced by several crucial factors (e.g., presence of people, furniture, random users' device orientation and the limited field of view (FOV) of optical receivers) which may contribute to the blockage of the line-of-sight (LOS) link. In this thesis, it is investigated whether deep learning (DL) techniques can effectively learn the distinct features of the indoor LiFi environment in order to provide superior performance compared to the conventional channel estimation techniques (e.g., minimum mean square error (MMSE) and least squares (LS)). This performance can be seen particularly when access to real-time channel state information (CSI) is restricted and is achieved with the cost of collecting large and meaningful data to train the DL neural networks and the training time which was conducted offline. Two DL-based schemes are designed for signal detection and resource allocation where it is shown that the proposed methods were able to offer close performance to the optimal conventional schemes and demonstrate substantial gain in terms of bit-error ratio (BER) and throughput especially in a more realistic or complex indoor environment. Performance analysis of LiFi networks under the influence of fixed and random blockers is essential and efficient solutions capable of diminishing the blockage effect is required. In this thesis, a CSI acquisition technique for a reconfigurable intelligent surface (RIS)-aided LiFi network is proposed to significantly reduce the dimension of the decision variables required for RIS beamforming. Furthermore, it is shown that several RIS attributes such as shape, size, height and distribution play important roles in increasing the network performance. Finally, the performance analysis for an RIS-aided realistic indoor LiFi network are presented. The proposed RIS configuration shows outstanding performances in reducing the network outage probability under the effect of blockages, random device orientation, limited receiver's FOV, furniture and user behavior. Establishing a LOS link that achieves uninterrupted wireless connectivity in a realistic indoor environment can be challenging. In this thesis, an analysis of link blockage is presented for an indoor LiFi system considering fixed and random blockers. In particular, novel analytical framework of the coverage probability for a single source and multi-source are derived. Using the proposed analytical framework, link blockages of the indoor LiFi network are carefully investigated and it is shown that the incorporation of multiple sources and RIS can significantly reduce the LOS coverage blockage probability in indoor LiFi systems

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Towards addressing training data scarcity challenge in emerging radio access networks: a survey and framework

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    The future of cellular networks is contingent on artificial intelligence (AI) based automation, particularly for radio access network (RAN) operation, optimization, and troubleshooting. To achieve such zero-touch automation, a myriad of AI-based solutions are being proposed in literature to leverage AI for modeling and optimizing network behavior to achieve the zero-touch automation goal. However, to work reliably, AI based automation, requires a deluge of training data. Consequently, the success of the proposed AI solutions is limited by a fundamental challenge faced by cellular network research community: scarcity of the training data. In this paper, we present an extensive review of classic and emerging techniques to address this challenge. We first identify the common data types in RAN and their known use-cases. We then present a taxonomized survey of techniques used in literature to address training data scarcity for various data types. This is followed by a framework to address the training data scarcity. The proposed framework builds on available information and combination of techniques including interpolation, domain-knowledge based, generative adversarial neural networks, transfer learning, autoencoders, fewshot learning, simulators and testbeds. Potential new techniques to enrich scarce data in cellular networks are also proposed, such as by matrix completion theory, and domain knowledge-based techniques leveraging different types of network geometries and network parameters. In addition, an overview of state-of-the art simulators and testbeds is also presented to make readers aware of current and emerging platforms to access real data in order to overcome the data scarcity challenge. The extensive survey of training data scarcity addressing techniques combined with proposed framework to select a suitable technique for given type of data, can assist researchers and network operators in choosing the appropriate methods to overcome the data scarcity challenge in leveraging AI to radio access network automation

    Modern meat: the next generation of meat from cells

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    Modern Meat is the first textbook on cultivated meat, with contributions from over 100 experts within the cultivated meat community. The Sections of Modern Meat comprise 5 broad categories of cultivated meat: Context, Impact, Science, Society, and World. The 19 chapters of Modern Meat, spread across these 5 sections, provide detailed entries on cultivated meat. They extensively tour a range of topics including the impact of cultivated meat on humans and animals, the bioprocess of cultivated meat production, how cultivated meat may become a food option in Space and on Mars, and how cultivated meat may impact the economy, culture, and tradition of Asia

    Évaluation et modulation des fonctions exécutives en neuroergonomie - Continuums cognitifs et expérimentaux

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    Des études en neuroergonomie ont montré que le pilote d’avion pouvait commettre des erreurs en raison d’une incapacité transitoire à faire preuve de flexibilité mentale. Il apparait que certains facteurs, tels qu’une forte charge mentale ou une pression temporelle importante, un niveau de stress trop élevé, la survenue de conflits, ou une perte de conscience de la situation, peuvent altérer temporairement l’efficience des fonctions exécutives permettant cette flexibilité. Depuis mes travaux initiaux, dans lesquels je me suis intéressé aux conditions qui conduisent à une négligence auditive, j’ai souhaité développer une approche scientifique visant à quantifier et limiter les effets délétères de ces différents facteurs. Ceci a été fait à travers l’étude des fonctions exécutives chez l’être humain selon le continuum cognitif (du cerveau lésé au cerveau en parfait état de fonctionnement) et le continuum expérimental (de l’ordinateur au monde réel). L’approche fondamentale de l’étude des fonctions exécutives en neurosciences combinée à l’approche neuroergonomique graduelle avec des pilotes et des patients cérébro-lésés, a permis de mieux comprendre la manière dont ces fonctions sont mises en jeu et altérées. Cette connaissance à contribuer par la suite à la mise en place de solutions pour préserver leur efficacité en situation complexe. Après avoir rappelé mon parcours académique, je présente dans ce manuscrit une sélection de travaux répartis sur trois thématiques de recherche. La première concerne l’étude des fonctions exécutives impliquées dans l’attention et en particulier la façon dont la charge perceptive et la charge mentale peuvent altérer ces fonctions. La deuxième correspond à un aspect plus appliqué de ces travaux avec l’évaluation de l’état du pilote. Il a été question d’analyser cet état selon l’activité de pilotage elle-même ou à travers la gestion et la supervision d’un système en particulier. La troisième et dernière thématique concerne la recherche de marqueurs prédictifs de la performance cognitive et l’élaboration d’entraînements cognitifs pour limiter les troubles dysexécutifs, qu’ils soient d’origine contextuelle ou lésionnelle. Ces travaux ont contribué à une meilleure compréhension des troubles cognitifs transitoires ou chroniques, mais ils ont aussi soulevé des questions auxquelles je souhaite répondre aujourd’hui. Pour illustrer cette réflexion, je présente en dernière partie de ce document mon projet de recherche qui vise à développer une approche multifactorielle de l’efficience cognitive, éthique et en science ouverte

    Ultra-Wideband Trained Artificial Neural Networks for Bluetooth Proximity Detection in Small Crowded Areas

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    Estimating the distance between indoor users is increasingly important in unexpected ways. One specific example is the need for electronic contact tracing demonstrated during the recent global pandemic. Smartphones are now routinely equipped with Bluetooth Low Energy radios, among other sensors, and these can be used for proximity detection based on received signal strength that is subject to errors due to poor modelling of the indoor propagation environment. Some high-end smartphones have now also been equipped with ultra-wideband ranging radios that provide a much more precise range measurement. This thesis demonstrates the concept of using a limited number of UWB-equipped smartphones to gather data to train Artificial Neural Networks (ANN) to improve short-range distance estimation among Bluetooth users. The trained RSSI to range model can be used for proximity determination by other Bluetooth users in small, crowded areas. Two ANN algorithms were trained using RSSI measurements from three BLE advertising channels and UWB range as ground truth and training data. The initial training and testing were conducted in a semi-empty office laboratory with 2130 observations. The RF model used 1917 samples (90% of data) for training and 213 samples (10%) for testing, while the CNN method used 1704 samples (80% of data) for training and 426 samples (20%) for evaluation. The trained neural network models were tested in two other office environments under different user conditions. The results indicate that the ANN models can estimate proximity in a new environment without further training with a mean error of less than 1.2 metres, within a range of up to 6 metres at line-of-sight (LOS). In highly constrained non-line-of-sight (NLOS) areas in the first office room, the proposed models provided proximity accuracy better than 2.9 metres. Furthermore, during testing across two adjacent office environments, each containing a single BLE device with complex furniture arrangements, the ANN models showed the proximity between the BLE devices with an error of less than 2-3 metres

    Detect to Learn: Structure Learning with Attention and Decision Feedback for MIMO-OFDM Receive Processing

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    The limited over-the-air (OTA) pilot symbols in multiple-input-multiple-output orthogonal-frequency-division-multiplexing (MIMO-OFDM) systems presents a major challenge for detecting transmitted data symbols at the receiver, especially for machine learning-based approaches. While it is crucial to explore effective ways to exploit pilots, one can also take advantage of the data symbols to improve detection performance. Thus, this paper introduces an online attention-based approach, namely RC-AttStructNet-DF, that can efficiently utilize pilot symbols and be dynamically updated with the detected payload data using the decision feedback (DF) mechanism. Reservoir computing (RC) is employed in the time domain network to facilitate efficient online training. The frequency domain network adopts the novel 2D multi-head attention (MHA) module to capture the time and frequency correlations, and the structural-based StructNet to facilitate the DF mechanism. The attention loss is designed to learn the frequency domain network. The DF mechanism further enhances detection performance by dynamically tracking the channel changes through detected data symbols. The effectiveness of the RC-AttStructNet-DF approach is demonstrated through extensive experiments in MIMO-OFDM and massive MIMO-OFDM systems with different modulation orders and under various scenarios.Comment: Accepted to IEEE Transactions on Communication

    Switchable wideband receiver frontend for 5G and satellite applications

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    Modern day communication architectures provides the requirement for interconnected devices offering very high data rate (more than 10 Gbps), low latency, and support for multiple service integration across existing communication generations with wideband spectrum coverage. An integrated satellite and 5G architecture switchable receiver frontend is presented in this thesis, consisting of a single pole double throw (SPDT) switch and two low noise amplifiers (LNAs) spanning X-band and K/Ka-band frequencies. The independent X-band LNA (8-12 GHz) has a gain of 38 dB at a centre design frequency of 9.8 GHz, while the K/Ka-band (23-28 GHz) has a gain of 29 GHz at a centre design frequency of 25.4 GHz. Both LNAs are a three-stage cascaded design with separated gate and drain lines for each transistor stage. The broadband high isolation single pole double throw (SPDT) switch based on a 0.15 μm gate length Indium Gallium Arsenide (InGaAs) pseudomorphic high electron transistor (pHEMT) is designed to operate at the frequency range of DC-50 GHz with less than 3 dB insertion loss and more than 40 dB isolation. The switch is designed to improve the overall stability of the system and the gain. A gain of about 25 dB is achieved at 9.8 GHz when the X-band arm is turned on and the K/Ka-band is turned off. A gain of about 23 dB is achieved at 25.4 GHz when the K/Ka-band arm is turned on and the X-band arm is off. This presented switchable receiver frontend is suitable for radar applications, 5G mobile applications, and future broadband receivers in the millimetre wave frequency range

    Security and Privacy for Modern Wireless Communication Systems

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    The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks
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