207 research outputs found
Assessing the Feasibility of Wireless Networks for Managed Automated Driving (MAD): A Spotlight on Communication Technology
The primary objective of this research is to develop a comprehensive understanding of the interplay between Signal-to-noise ratio (SNR) and Packet error rate (PER) and their implications on the overall performance of wireless communication systems. This thesis focuses to implement wireless communication between the remote infrastructureand vehicle using the User Datagram Protocol (UDP), with focus on
the physical, data link layer
Applications of graph theory to wireless networks and opinion analysis
La teoría de grafos es una rama importante dentro de la matemática discreta. Su uso ha aumentado recientemente dada la conveniencia de los grafos para estructurar datos, para analizarlos y para generarlos a través de modelos. El objetivo de esta tesis es aplicar teoría de grafos a la optimización de redes inalámbricas y al análisis de opinión. El primer conjunto de contribuciones de esta tesis versa sobre la aplicación de teoría de grafos a redes inalámbricas. El rendimiento de estas redes depende de la correcta distribución de canales de frecuencia en un espacio compartido. Para optimizar estas redes se proponen diferentes técnicas, desde la aplicación de heurísticas como simulated annealing a la negociación automática. Cualquiera de estas técnicas requiere un modelo teórico de la red inalámbrica en cuestión. Nuestro modelo de redes Wi-Fi utiliza grafos geométricos para este propósito. Los vértices representan los dispositivos de la red, sean clientes o puntos de acceso, mientras que las aristas representan las señales entre dichos dispositivos. Estos grafos son de tipo geométrico, por lo que los vértices tienen posición en el espacio, y las aristas tienen longitud. Con esta estructura y la aplicación de un modelo de propagación y de uso, podemos simular redes inalámbricas y contribuir a su optimización. Usando dicho modelo basado en grafos, hemos estudiado el efecto de la interferencia cocanal en redes Wi-Fi 4 y mostramos una mejora de rendimiento asociada a la técnica de channel bonding cuando se usa en regiones donde hay por lo menos 13 canales disponibles. Por otra parte, en esta tesis doctoral hemos aplicado teoría de grafos al análisis de opinión dentro de la línea de investigación de SensoGraph, un método con el que se realiza un análisis de opinión sobre un conjunto de elementos usando grafos de proximidad, lo que permite manejar grandes conjuntos de datos. Además, hemos desarrollado un método de análisis de opinión que emplea la asignación manual de aristas y distancias en un grafo para estudiar la similaridad entre las muestras dos a dos. Adicionalmente, se han explorado otros temas sin relación con los grafos, pero que entran dentro de la aplicación de las matemáticas a un problema de la ingeniería telemática. Se ha desarrollado un sistema de votación electrónica basado en mixnets, secreto compartido de Shamir y cuerpos finitos. Dicha propuesta ofrece un sistema de verificación numérico novedoso a la vez que mantiene las propiedades esenciales de los sistemas de votación
Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico
Conference proceedings info:
ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies
Raleigh, HI, United States, March 24-26, 2023
Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center
of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementación sistemática de la telemedicina dentro de un gran centro de evaluación de COVID-19 en el área de Baja California, México. Nuestro modelo se basa en factores de diseño centrados en el ser humano y colaboraciones interdisciplinarias para la habilitación escalable basada en datos de tecnologías de teleconsulta de teléfonos inteligentes, celulares y video para vincular hospitales, clínicas y servicios médicos de emergencia para evaluaciones de COVID en el punto de atención. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rápidamente se creó un equipo multidisciplinario, en cooperación con diferentes instituciones, entre ellas: la Universidad Autónoma de Baja California, la Secretaría de Salud, el Centro de Comando, Comunicaciones y Control Informático.
de la Secretaría del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psicólogos. Nuestro objetivo es proporcionar información al público y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignación de recursos con la anticipación de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-
Mobile 5G millimeter-wave multi-antenna systems
In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Universitat Politècnica de Catalunya's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink.Tesi en modalitat de compendi de publicacionsMassive antenna architectures and millimeter-wave bands appear on the horizon as the enabling technologies of future broadband wireless links, promising unprecedented spectral efficiency and data rates. In the recently launched fifth generation of mobile communications, millimetric bands are already introduced but their widespread deployment still presents several feasibility issues.
In particular, high-mobility environments represent the most challenging scenario when dealing with directive patterns, which are essential for the adequate reception of signals at those bands. Vehicular communications are expected to exploit the full potential of future generations due to the massive number of connected users and stringent requirements in terms of reliability, latency, and throughput while moving at high speeds. This thesis proposes two solutions to completely take advantage of multi-antenna systems in those cases: beamwidth adaptation of cellular stations when tracking vehicular users based on positioning and Doppler information and a tailored radiation diagram from a panel-based system of antennas mounted on the vehicle.
Apart from cellular base stations and vehicles, a third entity that cannot be forgotten in future mobile communications are pedestrians. Past generations were developed around the figure of human users and, now, they must still be able to seamlessly connect with any other user of the network and exploit the new capabilities promised by 5G. The use of millimeter-waves is already been considered by handset manufacturers but the impact of the user (and the interaction with the phone) is drastically changed. The last part of this thesis is devoted to the study of human user dynamics and how they influence the achievable coverage with different distributed antenna systems on the phone.Les arquitectures massives d'antenes i les bandes mil·limètriques apareixen a l'horitzó com les tecnologies que impulsaran els futurs enllaços sense fils amb gran ample de banda i prometen una eficiència espectral i velocitat de transmissió sense precedents. A la recent cinquena generació de comunicacions mòbils, les bandes mil·limètriques ja en són una part constitutiva però el seu desplegament encara presenta certes dificultats. En concret, els entorns d'alta mobilitat representen el major repte quan es fan servir diagrames de radiació directius, els quals són essencials per una correcta recepció del senyal en aquestes bandes. S'espera que les comunicacions vehiculars delimitin les capacitats de les xarxes en futures generacions degut al gran nombre d'usuaris simultanis i els requeriments estrictes en termes de fiabilitat, retard i flux de dades mentre es mouen a grans velocitats. Aquesta tesi proposa dues solucions per tal d'explotar al màxim els sistemes de múltiples antenes en tals casos: un ample de feix adaptatiu de les estacions bases quan estiguin fent el seguiment d'un vehicle usuari basat en informació de la posició i el Doppler i el disseny d'un diagrama de radiació adequat al costat del vehicle basat en una estructura de múltiples panells muntats a l'estructura del mateix. A més de les estacions base i els vehicles, un tercer element que no pot ser obviat en aquests escenaris són els vianants. Les generacions anteriors van ser desenvolupades al voltant de la figura d'usuaris humans i ara han de seguir tenint la capacitat de connexió ininterrumpuda amb la resta d'usuaris i explotar les capacitats de 5G. L'ús de frequències mil·limètriques també es té en compte en la fabricació de telèfons mòbils però l'impacte de l'usuari és completament diferent. La última part de la tesis tracta l'estudi de les dinàmiques de l'usuari humà i com influeixen en la cobertura amb diferent sistemes distribuïts d'antenes.Postprint (published version
Indoor Positioning and Navigation
In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot
Applications of Non-Orthogonal Waveforms and Artificial Neural Networks in Wireless Vehicular Communications
Ph. D. ThesisWe live in an ever increasing world of connectivity. The need for highly robust,
highly efficient wireless communication has never been greater. As we seek to squeeze
better and better performance from our systems, we must remember; even though
our computing devices are increasing in power and efficiency, our wireless spectrum
remains limited.
Recently there has been an increasing trend towards the implementation of machine
learning based systems in wireless communications. By taking advantage of a neural
networks powerful non-linear computational capability, communication systems have
been shown to achieve reliable error free transmission over even the most dispersive of
channels. Furthermore, in an attempt to make better use of the available spectrum,
more spectrally efficient physical layer waveforms are gathering attention that trade
increased interference for lower bandwidth requirements. In this thesis, the performance
of neural networks that utilise spectrally efficient waveforms within harsh transmission
environments are assessed.
Firstly, we investigate and generate a novel neural network for use within a standards
compliant vehicular network for vehicle-to-vehicle communication, and assess its
performance practically in several of the harshest recorded empirical channel models using
a hardware-in-the-loop testing methodology. The results demonstrate the strength
of the proposed receiver, achieving a bit-error rate below 10−3 at a signal-to-noise ratio
(SNR) of 6dB.
Secondly, this is then further extended to utilise spectrally efficient frequency
division multiplexing (SEFDM), where we note a break away from the 802.11p vehicular
communication standard in exchange for a more efficient use of the available spectrum
that can then be utilised to service more users or achieve a higher data throughput.
It is demonstrated that the proposed neural network system is able to act as a joint
channel equaliser and symbol receiver with bandwidth compression of up to 60%
when compared to orthogonal frequency division multiplexing (OFDM). The effect
of overfitting to the training environment is also tested, and the proposed system is shown to generalise well to unseen vehicular environments with no notable impact on
the bit-error rate performance.
Thirdly, methods for generating inputs and outputs of neural networks from complex
constellation points are investigated, and it is reasoned that creating ‘split complex’
neural networks should not be preferred over ‘contatenated complex’ neural networks
in most settings. A new and novel loss function, namely error vector magnitude (EVM)
loss, is then created for the purposes of training neural networks in a communications
setting that tightly couples the objective function of a neural network during training to
the performance metrics of transmission when deployed practically. This loss function
is used to train neural networks in complex environments and is then compared to
popular methods from the literature where it is demonstrated that EVM loss translates
better into practical applications. It achieved the lowest EVM error, thus bit-error
rate, across all experiments by a margin of 3dB when compared to its closest achieving
alternative. The results continue and show how in the experiment EVM loss was able
to improve spectral efficiency by 67% over the baseline without affecting performance.
Finally, neural networks combined with the new EVM loss function are further
tested in wider communication settings such as visible light communication (VLC) to
validate the efficacy and flexibility of the proposed system. The results show that neural
networks are capable of overcoming significant challenges in wireless environments, and
when paired with efficient physical layer waveforms like SEFDM and an appropriate
loss function such as EVM loss are able to make good use of a congested spectrum.
The authors demonstrated for the first time in practical experimentation with SEFDM
that spectral efficiency gains of up to 50% are achievable, and that previous SEFDM
limitations from the literature with regards to number of subcarriers and size of the
transmit constellation are alleviated via the use of neural networksEPSRC, Newcastle Universit
Reinforcement Learning Approaches to Improve Spatial Reuse in Wireless Local Area Networks
The ubiquitous deployment of IEEE 802.11 based Wireless Local Area Networks (WLANs) or WiFi networks has resulted in dense deployments of Access Points (APs) in an effort to provide wireless links with high data rates to users. This, however, causes APs and users/stations to experience a higher interference level. This is because of the limited spectrum in which WiFi networks operate, resulting in multiple APs operating on the same channel. This in turn affects the signal-tonoise-plus interference ratio (SINR) at APs and users, leading to low data rates that limit their quality of service (QoS).
To improve QoS, interference management is critical. To this end, a key metric of interest is spatial reuse. A high spatial reuse means multiple transmissions are able to transmit concurrently, which leads to a high network capacity. One approach to optimize spatial reuse is by tuning the clear channel access (CCA) threshold employed by the carrier sense multiple access with collision avoidance (CSMA/CA) medium access control (MAC) protocol. Specifically, the CCA threshold of a node determines whether it is allowed to transmit after sensing the channel. A node may increase its CCA threshold, causing it to transmit even when there are other ongoing transmissions. Another parameter to be tuned is transmit power. This helps a transmitting node lower its interference to neighboring cells, and thus allows nodes in these neighboring cells to transmit as well. Apart from that, channel bonding can be applied to improve transmission rate. In particular, by combining/aggregating multiple channels together, the resulting channel has a proportionally higher data rate than the case without channel bonding. However, the issue of spatial reuse remains the same whereby the focus is to maximize the number of concurrent transmissions across multiple channels
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Learning for Network Applications and Control
The emergence of new Internet applications and technologies have resulted in an increased complexity as well as a need for lower latency, higher bandwidth, and increased reliability. This ultimately results in an increased complexity of network operation and management. Manual management is not sufficient to meet these new requirements.
There is a need for data driven techniques to advance from manual management to autonomous management of network systems. One such technique, Machine Learning (ML), can use data to create models from hidden patterns in the data and make autonomous modifications. This approach has shown significant improvements in other domains (e.g., image recognition and natural language processing). The use of ML, along with advances in programmable control of Software- Defined Networks (SDNs), will alleviate manual network intervention and ultimately aid in autonomous network operations. However, realizing a data driven system that can not only understand what is happening in the network but also operate autonomously requires advances in the networking domain, as well as in ML algorithms.
In this thesis, we focus on developing ML-based network architectures and data driven net- working algorithms whose objective is to improve the performance and management of future networks and network applications. We focus on problems spanning across the network protocol stack from the application layer to the physical layer. We design algorithms and architectures that are motivated by measurements and observations in real world or experimental testbeds.
In Part I we focus on the challenge of monitoring and estimating user video quality of experience (QoE) of encrypted video traffic for network operators. We develop a system for REal-time QUality of experience metric detection for Encrypted Traffic, Requet. Requet uses a detection algorithm to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a random forest ML model to predict QoE metrics. We evaluate Requet on a YouTube dataset we collected, consisting of diverse video assets delivered over various WiFi and LTE network conditions. We then extend Requet, and present a study on YouTube TV live streaming traffic behavior over WiFi and cellular networks covering a 9-month period. We observed pipelined chunk requests, a reduced buffer capacity, and a more stable chunk duration across various video resolutions compared to prior studies of on-demand streaming services. We develop a YouTube TV analysis tool using chunks statistics detected from the extracted data as input to a ML model to infer user QoE metrics.
In Part II we consider allocating end-to-end resources in cellular networks. Future cellular networks will utilize SDN and Network Function Virtualization (NFV) to offer increased flexibility for network infrastructure operators to utilize network resources. Combining these technologies with real-time network load prediction will enable efficient use of network resources. Specifically, we leverage a type of recurrent neural network, Long Short-Term Memory (LSTM) neural networks, for (i) service specific traffic load prediction for network slicing, and (ii) Baseband Unit (BBU) pool traffic load prediction in a 5G cloud Radio Access Network (RAN). We show that leveraging a system with better accuracy to predict service requirements results in a reduction of operation costs.
We focus on addressing the optical physical layer in Part III. Greater network flexibility through SDN and the growth of high bandwidth services are motivating faster service provisioning and capacity management in the optical layer. These functionalities require increased capacity along with rapid reconfiguration of network resources. Recent advances in optical hardware can enable a dramatic reduction in wavelength provisioning times in optical circuit switched networks. To support such operations, it is imperative to reconfigure the network without causing a drop in service quality to existing users. Therefore, we present a ML system that uses feedforward neural networks to predict the dynamic response of an optically circuit-switched 90-channel multi-hop Reconfigurable Optical Add-Drop Multiplexer (ROADM) network. We show that the trained deep neural network can recommend wavelength assignments for wavelength switching with minimal power excursions. We extend the performance of the ML system by implementing and testing a Hybrid Machine Learning (HML) model, which combines an analytical model with a neural network machine learning model to achieve higher prediction accuracy.
In Part IV, we use a data-driven approach to address the challenge of wireless content delivery in crowded areas. We present the Adaptive Multicast Services (AMuSe) system, whose objective is to enable scalable and adaptive WiFi multicast. Specifically, we develop an algorithm for dynamic selection of a subset of the multicast receivers as feedback nodes. Further, we describe the Multicast Dynamic Rate Adaptation (MuDRA) algorithm that utilizes AMuSe’s feedback to optimally tune the physical layer multicast rate. Our experimental evaluation of MuDRA on the ORBIT testbed shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of nodes while meeting quality requirements. We leverage the lessons learned from AMuSe for WiFi and use order statistics to address the performance issues with LTE evolved Multimedia Broadcast/Multicast Service (eMBMS). We present the Dynamic Monitoring (DyMo) system which provides low-overhead and real-time feedback about eMBMS performance to be used for network optimization. We focus on the Quality of Service (QoS) Evaluation module and develop a Two-step estimation algorithm which can efficiently identify the SNR Threshold as a one time estimation. DyMo significantly outperforms alternative schemes based on the Order-Statistics estimation method which relies on random or periodic sampling
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