Search

Presentació de la demo de l'assignatura de Seguretat i Administració de Xarxes. dedicada a la petjada digital, les xarxes sense fils i la privacitat, 2023/2024, 2n quadrimestre

The present work aims to design and implement a parking assistance system for articulated vehicles called MASDAV (Manoeuvre Assistance System with Drones for Articulated Vehicles). The system is based on the use of a mini drone that deploys when the driver wishes to park the vehicle. This drone provides a bird's-eye view of the vehicle and its maneuvers, thus facilitating the parking process and reducing the risk of collisions. The methodology used includes SITL (Software In The Loop) configuration to simulate the drone's flight environment and integration with QGroundControl to plan and execute take-off, vehicle tracking and landing missions. A Python script has been developed using the dronekit library to control the drone autonomously, simulating parking scenarios and adjusting the height and position of the drone in real time. In addition, the basis is laid for a practical implementation of the proposed mechanisms. For possible implementation, the Holybro X500 V2 drone, compatible with the Ardupilot platform, which offers advanced flight control capabilities and precise navigation via GPS RTK, is recommended. Communication between the drone and the vehicle's navigation system is carried out via radio frequency, ensuring fast and reliable data transmission, even in adverse conditions. The on-board computer chosen is the NVIDIA Jetson Xavier NX, which provides the processing power needed to handle real-time image monitoring and analysis tasks. Ubuntu has been selected as the operating system because of its compatibility with drone control software. The results obtained show that the MASDAV system is capable of providing effective assistance in the parking of articulated vehicles. The drone can track the vehicle precisely, adjusting its position to maintain an optimal view. In the event of signal loss or low battery, the drone automatically returns to its bay on the roof of the vehicle, ensuring the safety and continuity of the system. In addition, it has been prov, Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura

The main purpose of this thesis is to explore the benefits of combining two radio interfaces onboard a Unmanned Aerial Vehicle (UAV) to communicate with a Ground Control Station (GCS) and other UAVs inside a swarm. The goals are to use the IEEE 802.11ah standard (Wi-Fi HaLow) with other Wi-Fi technologies, such as IEEE 802.11ax specification (Wi-Fi 6/6E), to enable real-time video transmission from UAVs to the GCS. Several scenarios in which an UAV swarm performs infrastructure inspection are emulated. During the missions, UAVs have to send real-time video to the GCS through a multihop network when some damage in the infrastructure is found. The emulated scenarios are defined using different architectures and radio technologies. Once the emulations finish, the collected data is transferred to spreadsheets to be analysed. An expression to find the multi-hop network capacity from hops distances is found using the NS-3 simulator and this expression is used to determine the capacity of the multi-hop networks used in the emulations. Then, this expression is applied in the algorithms in charge of composing the multi-hop network to offer on demand quality video. This contribution among others related to new techniques and strategies to build the multi-hop network, are implemented in the emulation program. After performing the emulations with the proposed strategies, the resulting data is analysed and compared among the different scenarios., Objectius de Desenvolupament Sostenible::8 - Treball Decent i Creixement Econòmic, Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura

In the modern world, the use of Internet based communications has become a daily activity for a big part of the population. In particular, mobile communications are experiencing a relentless growth in the number of users, which has become an issue as they must be allocated in the channels with the limited available time and frequency resources in the physical layer. This is why Non-Orthogonal Multiple Access (NOMA) schemes are being used in 5G mobile communications and are expected to be a key factor in the future 6G communications. Another factor to take into account is the evolution in technologies such as Artificial Intelligence (AI) and Machine Learning (ML) that, as proven in literature, can perform tasks in the telecommunications world. NOMA will play an important role in 6G communications, while AI/ML has proven to play a key role in many layers including the physical layer, to improve the performance. In particular, constellation design to optimize BER vs SNR performance has proven to be feasible via Deep Learning (DL). In this study, we focus on neural design of demappers for an uplink NOMA system, to demonstrate the performance advantages of these systems against classical demapping techniques. The traditional demapping techniques to evaluate are the Successive Interference Cancellation (SIC) and the Joint Demapper. On the other hand, the SICNet demapper , a ML version of the traditional SIC, has been adapted to the uplink NOMA system. The comparison of the performance of all the demappers has been done with BER-EbN0 curves. The results show that the Joint demapper is the most optimal technique in a two-user uplink system. Meanwhile, the SICNet demapper has proven to outperform the SIC demapper in most of the studied scenarios.

The following project is based on the study of different solutions to manage a drone swarm by implementing a Wi-Fi HaLow control interface. To do this, a case study is carried out where a number of drones are deployed on the runway of Josep Tarradellas Barcelona-El Prat airport using different strategies. Then, an analysis is made of the different parameters obtained in order to determine which of the proposed strategies best suits the different scenarios., Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura

This final degree thesis is an analysis of the IEEE 802.11ax standard and the two subsequent certifications, Wi-Fi 6 and Wi-Fi 6E, with the aim of obtaining information to understand these new IEEE 802.11 standards. We will start by presenting the evolution of the IEEE 802.11 standard throughout its history, detailing the main and most important features such as frequencies, bandwidths, and maximum transmission rates. Then, we will discuss the IEEE 802.11ax specification, distinguishing the mechanisms defined at the physical layer (PHY) and the medium access control layer (MAC). Afterwards, we will proceed with the performance analysis, starting with the description of the scenarios, where we will explain the specifications of the elements used (access points, and adapters) to work in this new frequency band and the dispositions of each of the scenarios. Also, with the help of a spectrum analyser, a visualization of the spectrum found in the 3 bands will be made, which will help us to see how congested they are. The analysis will consist of tests carried out in a laboratory to measure, mainly, the maximum throughput, outdoor tests to measure throughput as a function of distance and, finally, an analysis of the association time will be carried out for the different frequency bands., Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura

The IEEE 802.11ah standard is intended to adapt the specifications of IEEE 802.11 to the Internet of Things (IoT) scenario. One of the main features of IEEE 802.11ah consists of the Restricted Access Window (RAW) mechanism, designed for scheduling transmissions of groups of stations within certain periods of time or windows. With an appropriate configuration, the RAW feature reduces contention and improves energy efficiency. However, the standard specification does not provide mechanisms for the optimal setting of RAW parameters. In this way, this paper presents a grouping strategy based on a genetic algorithm (GA) for IEEE 802.11ah networks operating under the RAW mechanism and considering heterogeneous stations, that is, stations using different modulation and coding schemes (MCS). We define a fitness function from the combination of the predicted system throughput and fairness, and provide the tuning of the GA parameters to obtain the best result in a short time. The paper also includes a comparison of different alternatives with regard to the stages of the GA, i.e., parent selection, crossover, and mutation methods. As a proof of concept, the proposed GA-based RAW grouping is tested on a more constrained device, a Raspberry Pi 3B+ , where the grouping method converges in around 5 s. The evaluation concludes with a comparison of the GA-based grouping strategy with other grouping approaches, thus showing that the proposed mechanism provides a good trade-off between throughput and fairness performance., Postprint (published version)

Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura

Information and communications technology (ICT) engineers play a key part in our society's digitization process. To foster that role, the Castelldefels School of Telecommunications and Aerospace Engineering (EETAC) of UPC's Degree in Network Engineering currently includes a new competence termed digital awareness. This report demonstrates the initiative's first outcomes, including the teaching resources created, and feedback from students and teachers. In addition, the findings of a high school poll are presented, indicating a high level of interest in this new competence.

The explosion in data traffic, the physical resource constraints, and the insufficient financial incentives for deploying 5G networks, stress the need for a paradigm shift in network upgrades. Typically, operators are also the service providers, which charge the end users with low and flat tariffs, independently of the service enjoyed. A fine-scale management of the network resources is needed, both for optimizing costs and resource utilization, as well as for enabling new synergies among network owners and third-parties. In particular, operators could open their networks to third parties by means of fine-scale sharing agreements over customized networks for enhanced service provision, in exchange for an adequate return of investment for upgrading their infrastructures. The main objective of this thesis is to study the potential of fine-scale resource management and sharing mechanisms for enhancing service provision and for contributing to a sustainable road to 5G. More precisely, the state-of-the-art architectures and technologies for network programmability and scalability are studied, together with a novel paradigm for supporting service diversity and fine-scale sharing. We review the limits of conventional networks, we extend existing standardization efforts and define an enhanced architecture for enabling 5G networks' features (e.g., network-wide centralization and programmability). The potential of the proposed architecture is assessed in terms of flexible sharing and enhanced service provision, while the advantages of alternative business models are studied in terms of additional profits to the operators. We first study the data rate improvement achievable by means of spectrum and infrastructure sharing among operators and evaluate the profit increase justified by a better service provided. We present a scheme based on coalitional game theory for assessing the capability of accommodating more service requests when a cooperative approach is adopted, and for st, La explosion del trafico de datos, los recursos limitados y la falta de incentivos para el desarrollo de 5G evidencian la necesidad de un cambio de paradigma en la gestion de las redes actuales. Los operadores de red suelen ser tambien proveedores de servicios, cobrando tarifas bajas y planas, independientemente del servicio ofrecido. Se necesita una gestion de recursos precisa para optimizar su utilizacion, y para permitir nuevas sinergias entre operadores y proveedores de servicios. Concretamente, los operadores podrian abrir sus redes a terceros compartiendolas de forma flexible y personalizada para mejorar la calidad de servicio a cambio de aumentar sus ganancias como incentivo para mejorar sus infraestructuras. El objetivo principal de esta tesis es estudiar el potencial de los mecanismos de gestion y comparticion de recursos a pequei\a escala para trazar un camino sostenible hacia el 5G. En concreto, se estudian las arquitecturas y tecnolog fas mas avanzadas de "programabilidad" y escalabilidad de las redes, junto a un nuevo paradigma para la diversificacion de servicios y la comparticion de recursos. Revisamos los limites de las redes convencionales, ampliamos los esfuerzos de estandarizacion existentes y definimos una arquitectura para habilitar la centralizacion y la programabilidad en toda la red. La arquitectura propuesta se evalua en terminos de flexibilidad en la comparticion de recursos, y de mejora en la prestacion de servicios, mientras que las ventajas de un modelo de negocio alternativo se estudian en terminos de ganancia para los operadores. En primer lugar, estudiamos el aumento en la tasa de datos gracias a un uso compartido del espectro y de las infraestructuras, y evaluamos la mejora en las ganancias de los operadores. Presentamos un esquema de admision basado en la teoria de juegos para acomodar mas solicitudes de servicio cuando se adopta un enfoque cooperativo, y para estudiar las condiciones para que la reparticion de recursos sea convenie, Postprint (published version)

Older generations of wireless networks, such as 1G and 2G were deployed using leased line, copper or fibre line as backhaul. Later, in 3G and 4G, microwave wireless links have also worked as backhaul links while the backbone of the network was still wireline-based. However, due to multiple different use cases and deployment scenarios of 5G, solo wireline based backhaul network is not a cost-efficient option for the operators anymore. For cost-efficient and fast deployment, wireless backhaul options are very attractive. As drawbacks, wireless backhaul links have capacity and distance limitations. To take the advantages of both the solutions, i.e., wired and wireless, 5G transport networks are anticipated to be a heterogeneous, complex, and with stringent performance requirements. To address the aforementioned challenges, wireless backhaul options are providing more attractive solutions, and hence, technologies using the same resources (e.g., frequency channels) may be used by both access and backhaul networks. In this scenario, blurring the separation line between access and backhaul networks allows resource sharing and cooperation between both the networks and minimizes the network deployment and maintenance cost significantly. Therefore, in 5G, the access and backhaul networks cannot be seen as separate entities; rather, we seek to integrate them together to ensure the best use of resources. In this thesis, firstly, we investigate the challenges and potential technologies of 5G transport network. Later, to address these challenges, we identify and present different approaches to perform joint access-backhaul mechanism. An initial performance evaluation of access-aware backhaul optimization is presented, where backhaul network is dynamically assigned with the required resources to serve the dynamic requirements of a 5G access network. The evaluation results and discussions manifest the resource efficiency of joint access-backhaul mechanisms. Functional splits in dif, Las primeras generaciones de redes móviles, se implementaron utilizando líneas de cobre o fibra para la conexión entre la red de acceso y el núcleo de la red (conexión backhaul). Más tarde, los enlaces inalámbricos también han funcionado como backhaul mientras que la columna vertebral de la red seguía basada en cable. Sin embargo, debido a los múltiples escenarios de implementación de 5G, una red de backhaul basada solamente en cable ya no es una opción rentable para los operadores. Para una implementación rentable y rápida, las opciones de backhaul inalámbrico son muy atractivas. Como inconvenientes, los enlaces backhaul inalámbricos tienen limitaciones de capacidad y distancia. Para aprovechar las ventajas de ambas soluciones, es decir, cableadas e inalámbricas, se prevé que las redes de transporte 5G sean heterogéneas, complejas y con estrictos requisitos de rendimiento. Para abordar los desafíos antes mencionados, las opciones de backhaul inalámbrico brindan soluciones más atractivas y, por lo tanto, las tecnologías que usan los mismos recursos (por ejemplo, canales de frecuencia) pueden usarse tanto en las redes de acceso como en las de backhaul. En este escenario, desdibujar la línea de separación entre las redes de acceso y backhaul permite el intercambio de recursos y la cooperación entre ambas redes, y minimiza significativamente los costes de implementación y mantenimiento de la red. Por lo tanto, en 5G las redes de acceso y backhaul no pueden verse como entidades separadas; más bien consideraremos su integración para asegurar el mejor uso de los recursos. En esta tesis, en primer lugar, investigamos los desafíos y las tecnologías potenciales para la implementación de la red de backhaul 5G. Más tarde, para abordar dichos desafíos, identificamos diferentes enfoques para un mecanismo conjunto de gestión de la red de acceso y backhaul. Se presenta una evaluación de rendimiento inicial para la optimización de backhaul que tiene en cuenta el estado de la red de, Postprint (published version)

The healthcare Internet of Things (H-IoT) is an interconnection of devices capable of sensing and transmitting information that conveys the status of an individual’s health. The continuous monitoring of an individual’s health for disease diagnosis and early detection is an important application of H-IoT. Ambient-assisted living (AAL) entails monitoring a patient’s health to ensure their well-being. However, ensuring a limit on transmission delays is an essential requirement of such monitoring systems. The uplink (UL) transmission during the orthogonal frequency division multiple access (OFDMA) in the wireless local area networks (WLANs) can incur a delay which may not be acceptable for delay-sensitive applications such as H-IoT due to their random nature. Therefore, we propose a UL OFDMA scheduler for the next Wireless Fidelity (Wi-Fi) standard, the IEEE 802.11be, that is compliant with the latency requirements for healthcare applications. The scheduler allocates the channel resources for UL transmission taking into consideration the traffic class or access category. The results demonstrate that the proposed scheduler can achieve the required latency for H-IoT applications. Additionally, the performance in terms of fairness and throughput is also superior to state-of-the-art schedulers., This research was supported in part by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (NRF-2021R1A6A1A03039493), in part by the NRF grant funded by the Korea government (MSIT) (NRF-2022R1A2C1004401), and in part by Spanish MCIN/AEI/10.13039/501100011033 through project PID2019-106808RA-I00., Postprint (published version)

Information and communications technology (ICT) engineers play a key part in our society's digitization process. To foster that role, the Castelldefels School of Telecommunications and Aerospace Engineering (EETAC) of UPC's Degree in Network Engineering currently includes a new competence termed digital awareness. This report demonstrates the initiative's first outcomes, including the teaching resources created, and feedback from students and teachers. In addition, the findings of a high school poll are presented, indicating a high level of interest in this new competence., This work is supported by the teaching innovation project “Introduction of the competence of digital awareness in the degree of Degree in Telematics Engineering”, ICE-UPC 2021. The authors would like to express their gratitude to the "Applications Engineering" teachers and students, as well as the teachers and support personnel who made the high school survey possible., Peer Reviewed, Postprint (published version)

This work consists of finding a software simulator that allows the study of WLAN (Wireless Local Area Network) networks based on the 802.11 standard. To this end, it was decided to analyse the suitability of the Cisco Packet Tracer, GNS3 and OMNeT++ programs as wireless network simulation environments. The analysis consists of an introduction to the software, an installation tutorial, the setup of one or two simple scenarios and the conclusions after working with each software. More specifically, the introduction includes a brief explanation of the programme, that is to say, what language it is programmed in, whether it is free software or not, what types of users are used to working with it, what it is usually used for, etc. In the installation tutorial, all the necessary steps to have the software available on the computer are detailed, from the web page where it is recommended to download the executable, to the demonstration of some useful advice. In the assembly of the different scenarios, both the way in which the assembly is carried out and the programme itself are explained, i.e., the processes that must be followed to create each environment are specified and, at the same time, the most important parts of the programme are defined. Finally, in the conclusions, the most positive and negative points of each programme are expressed in the form of an opinion, considering the objective of this work. Once the three programs have been examined, it is concluded that the most suitable software for studying wireless networks through simulated environments is OMNeT++, as it is the program that allows the most detailed specifications related to the configuration of this type of networks. Therefore, a fourth chapter describes the results obtained from the simulation programmed with OMNeT++, which consists of two computers connected through an "ad-hoc" wireless network. Finally, the last section presents the general conclusions obtained after working with and studying the

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., For guaranteeing the strict requirements foreseen for 5G, network slicing has been proposed as a dynamic and scalable mechanism for the allocation of customized resources to service providers. Many solutions have been proposed in the literature for the scenario where multiple service providers share the same pool of resources, while the exclusive allocation to different providers is still an open issue due to the associated complexity. In this work, we define a policy-based admission mechanism for exclusive intraservice slice allocation, at fine and adaptable timescales. In particular, we consider the case where optimal admission strategies are pre-computed offline for network state conditions that are representative of typical traffic loads and resource availability. This offline phase is also used to train a Machine learning algorithm; a neural network (NN) learns the best admission policies from a more computationally expensive mechanism in previously studied network conditions. Thus, the NN is used for providing near-optimal admission decisions at runtime under network conditions for which no optimal policy has been computed. The potential of the 5G marketplace in terms of revenue and quality of service is demonstrated for the particular case of services with strict latency constraints by means of a proof of concept tested over network traces from a real network operator. Different strategies are compared for the computation of the admission strategies and results are provided in terms of efficiency in resource utilization, fairness to the service providers, network owners’ revenue and complexity. This study confirms the feasibility of a policy-based approach for exclusive intra-service resource allocation, especially if computationally-efficient mechanisms are adopted in the case of missing information about network states., This work was supported in part by the EU Horizon 2020 Research and Innovation Programme (5GAuRA) under Grant 675806, and in part by the Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya under Grant 2017 SGR 376., Peer Reviewed, Postprint (published version)

The fifth generation (5G) mobile networks are enabling operators and stakeholders to enhance and innovate new services in response to an increasing market demand. 5G architecture provides scalability and flexibility for adapting its infrastructure to a customizable communication system by means of Cloudification. Softwarization and virtualization are key terms for upcoming industries that will require ultra-low latency, only possible if the infrastructure equipment that traditionally was centralized in the communication network core is physically moved closer to the user, at the network edge. The main objective of this master thesis was to implement a Reinforcement Learning algorithm (Q-Learning Temporal Difference) aimed at next generation networks to optimally allocate Virtualized Network Functions (VNF) to 5G network Edge Computing (EC) centers. In order to evaluate the algorithm performance and compare it, two more algorithms have been developed to achieve a solution under the same network circumstances. The first one, Best Fit, was inspired by a classical network load balancing algorithm (Weighted Round Robin), whereas the second, MDP, was approached through dynamic programming (Policy Iteration), having posed the network dynamics as a finite Markov Decision Process. The several tests that have been carried out indicate that Q-Learning performs better than the Best Fit and almost as close as the MDP. It shows that the Q-Learning algorithm is able to allocate optimally the incoming VNF demands when EC centers' available resources are somehow restricted.

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., The popularity of mobile broadband connectivity continues to grow and thus, the future wireless networks are expected to serve a very large number of users demanding a huge capacity. Employing larger spectral bandwidth and installing more access points to enhance the capacity is not enough to tackle the stated challenge due to related costs and the interference issues involved. In this way, frequency resources are becoming one of the most valuable assets, which require proper utilization and fair distribution. Traditional frequency resource management strategies are often based on static approaches, and are agnostic to the instantaneous demand of the network. These static approaches tend to cause congestion in a few cells, whereas at the same time, might waste those precious resources on others. Therefore, such static approaches are not efficient enough to deal with the capacity challenge of the future network. Thus, in this paper we present a dynamic access-aware bandwidth allocation approach, which follows the dynamic traffic requirements of each cell and allocates the required bandwidth accordingly from a common spectrum pool, which gathers the entire system bandwidth. We perform the evaluation of our proposal by means of real network traffic traces. Evaluation results presented in this paper depict the performance gain of the proposed dynamic access-aware approach compared to two different traditional approaches in terms of utilization and served traffic. Moreover, to acquire knowledge about access network requirement, we present a machine learning-based approach, which predicts the state of the network, and is utilized to manage the available spectrum accordingly. Our comparative results show that, in terms of spectrum allocation accuracy and utilization efficiency, a well designed machine learning-based bandwidth allocation mechanism not only outperforms common static approaches, but even achieves the performance (with a relative error close to 0.04) of an ide, This work was supported in part by the EU Horizon 2020 Research and Innovation Program (5GAuRA) under Grant 675806, and in part by the Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement from the Generalitat de Catalunya under Grant 2017 SGR 376., Peer Reviewed, Postprint (published version)

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., The expected growth in carried traffic and the added complexity of different deployment approaches (distributed vs. centralized), showing different requirements, make transport network one of the main design challenges in 5G. One of those challenges is posed by the Centralized Radio Access Network (CRAN) paradigm, whereby different functionalities of the base station are split between a central unit and a remote unit, both connected by a fronthaul/midhaul network. When this centralization includes physical layer functions, stringent capacity and delay constraints are imposed on the fronthaul, thus making its design and deployment more challenging and costly. At this point, the anticipated capacity requirements for fronthaul links are enormous and, as of today, no single technology can support such requirements. Hence, the complex transport network will be heterogeneous in nature. There is consensus in following two approaches to tackle the fronthaul challenge: i) building a heterogeneous network by combining different technologies; and ii) employing different functional splits, which have the potential to reduce the capacity requirements on fronthaul links. Hence, it is important that we exploit different potential technologies and a functional split approach for 5G fronthaul networks design. As our contribution, we show how intelligently selected functional splits at physical layer can be utilized to serve the radio access networks in a capacity-limited scenario. From a different point of view, we also propose maximizing the centralization by means of a heterogeneous combination of functional splits in a budget-limited scenario. Results presented in this paper show that the combination of functional splits has the potential to enable the design of heterogeneous fronthaul networks combining wireless and wired links, and reducing drastically both the required capacity (to 40%) and the total cost of ownership (to 35%)., This work was supported in part by the EU Horizon 2020 Research and Innovation Program (5GAuRA) under Grant 675806, and in part by the Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement from the Generalitat de Catalunya under Grant 2017 SGR 376., Peer Reviewed, Postprint (published version)

As a result of the world’s population being much more digitally connected, it is increasingly relevant to acquire an adequate level of digital awareness. For this reason, the objective of this work is to introduce the competence of digital awareness in higher education programs. Specifically, this work is focused on the deployment of such competence within the Degree in Network Engineering of the Castelldefels School of Telecommunications and Aerospace Engineering of the Universitat Politecnica de Catalunya. To do this, we ` have planned a project structured in four phases in which the tasks to be carried out by the different participants have been defined in detail. Thus, the roles involved are: project coordinator, academic content designers, support companies for this implementation, students, as well as the Degree Coordinator and the Deputy Director of Master’s Studies and Head of Quality. This is the first initiative we plan to extend to other grades or levels of study in the future., Peer Reviewed, Postprint (published version)

IEEE 802.11 consists of one of the most used wireless access technologies, which can be found in almost all consumer electronics devices available. Recently, Wake-up Radio (WuR) systems have emerged as a solution for energy-efficient communications. WuR mechanisms rely on using a secondary low-power radio interface that is always in the active operation mode and is in charge of switching the primary interface, used for main data exchange, from the power-saving state to the active mode. In this paper, we present a WuR solution based on IEEE 802.11 technology employing transmissions of legacy frames by an IEEE 802.11 standard-compliant transmitter during a Transmission Opportunity (TXOP) period. Unlike other proposals available in the literature, the WuR system presented in this paper exploits the PHY characteristics of modern IEEE 802.11 radios, where different signal bandwidths can be used on a per-packet basis. The proposal is validated through the Matlab software tool, and extensive simulation results are presented in a wide variety of scenario configurations. Moreover, insights are provided on the feasibility of the WuR proposal for its implementation in real hardware. Our approach allows the transmission of complex Wake-up Radio signals (i.e., including address field and other binary data) from legacy Wi-Fi devices (from IEEE 802.11n-2009 on), avoiding hardware or even firmware modifications intended to alter standard MAC/PHY behavior, and achieving a bit rate of up to 33 kbps., Postprint (published version)

In the last few years, Machine learning algorithms have enabled the development of many applications, which are used by a lot of people on a daily basis. This applications provide functionalities in fields as diverse as radiology or virtual assistants with voice recognition. Many of this applications, would have been nearly impossible to develop without the progress made in the field among the last decades, but are now possible with machine learning algorithms. It is also likely, because of the current growth in this topic, that we will continue to see more and more applications being developed using machine learning in the next years. The main goal of this work is to find and explore potential applications of machine learning on the management of an airport. As a proof of concept, two particular applications have been deeply studied: face recognition and prediction of waiting times in airport queues. Regarding face recognition, there will be an edge computing-based development, implementation and evaluation and there will also be an implementation and evaluation of a cloud-based model, which uses Azure Face API. Both implementations are done using a Raspberry Pi, with the intent of comparing the run time and energy consumption of the two approaches. The obtained face recognition results are good in both edge and cloud models, but they are better in the cloud model, which is excellent in face recognition quality. Regarding run time and energy consumption, the cloud model also wins, with the best performance being when used with a cable internet connection between the camera-equipped end device and the network. With respect to the tool for predicting airport passport control waiting times, the chosen methodology involved different steps. A first phase, pre-processing public data of airport waiting times. Then, a second phase in which several machine learning algorithms are tested to evaluate which one performed the best. Finally, the chosen machine learning algorithm, Los algoritmos de machine learning han permitido desarrollar, durante los últimos años una gran cantidad de aplicaciones que son utilizadas por muchas personas a diario, estas proporcionan funcionalidades en campos tan diversos como la radiología o los asistentes virtuales controlados por voz. Muchas de estas aplicaciones, hubiesen sido prácticamente imposibles de desarrollar sin los avances realizados en esta materia durante las últimas décadas, pero con algoritmos de machine learning son posibles. El principal objetivo de este trabajo es encontrar y explorar aplicaciones potenciales de machine learning en la gestión de un aeropuerto. Como prueba de concepto, se han estudiado en profundidad dos aplicaciones: el reconocimiento facial y la predicción del tiempo de espera encolas en aeropuertos. En lo que respecta al reconocimiento facial, se desarrolla, implementa y evalúa un modelo de reconocimiento facial edge, y también se implementa y evalúa un modelo de reconocimiento facial cloud utilizando la Face API de Azure. Ambas implementaciones se realizan en Raspberry Pi, con la intención de comparar el tiempo y consumo de energía que se requiere para cada uno de los dos modelos.Los resultados de reconocimiento facial obtenidos son buenos en ambos casos, pero son superiores en el modelo cloud, tanto en calidad del reconocimiento, como en tiempo y consumo de energía, siendo la mejor opción utilizar el modelo cloud con una conexión a internet mediante cable.Respecto a la herramienta para predecir el tiempo de espera en cola en un aeropuerto, se ha requerido un procesado de datos públicos sobre tiempos de esperas en aeropuertos. A continuación, se ha hecho una comparación entre varios algoritmos para ver cuál daba mejores resultados y, finalmente, se ha optimizado el algoritmo que ha dado mejores resultados.Los resultados obtenidos con esta herramienta son prometedores, y permiten ampliar el modelo desarrollado en este trabajo para futuras aplicaciones relacionadas con las

IEEE 802.11ah is an Internet of Things enabling technology, where the efficient management of thousands of devices is a key function. To this aim, IEEE 802.11ah provides the restricted access window (RAW) mechanism, which reduces contention by enabling transmissions for small groups of stations. Optimal grouping of RAW stations requires an evaluation of many possible configurations. In this paper, we propose an analytical model (named e-model) that provides an evaluation of the RAW configuration performance, allowing a fast adaptation of RAW grouping policies, in accordance to varying channel conditions. We base the e-model in known saturation models, which we adapted to include the IEEE 802.11ah’s PHY and MAC layer modifications and to support different bitrates and packet sizes. As a proof of concept, we use the proposed model to compare the performance of different grouping strategies, showing that the e-model is a useful analysis tool in RAW-enabled scenarios. We validate the model with existing IEEE 802.11ah implementation for NS-3., Peer Reviewed, Postprint (author's final draft)

Incoming

La radiació electromagnètica de les xarxes sense fils és “no ionitzant”, és a dir, els seus fotons no porten prou energia com per produir danys La clau és la potència: un forn microones radia una potència prop d’1.000.000 de vegades superior a la d’un dispositiu mòbil, Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura, Postprint (published version)

The increasing need for faster, reliable and latency bounded solutions, especially in theindustrial, medical and audiovisual fields, alongside the demand for mobility and easeof configuration, make the fusion of IEEE 802.11 (“Wi-Fi”) and Time-Sensitive Networking(TSN) more necessary than ever. The evolution of high-quality video, the implementation oftelesurgery in health care and haptic technology in industries brings a wide variety of newchallenges in time synchronization, latency, reliability and management of resources. Toachieve such specifications, formerly all the transmissions were wired, as the wireless so-lutions could not compete especially in terms of reliability and bounded delay. Nowadays,considering the current most notorious iteration of the wireless communications standard,IEEE 802.11ac, with a slight look into the newest improvements in TSN for the upcomingversion IEEE 802.11be and the up-to-date publications for the TSN related amendmentsunder IEEE 802.1, it is obvious that there is a remarkable interest in enhancing the Wi-Fitechnology for these kinds of scenarios. In this work, the concepts of the two technologiesmentioned above are studied, specifically at the physical and link layers, to then develop asimulator with which to study the behaviour of a wireless system in response to a set of in-put parameters and its operation is validated according to different theoretical calculations.In addition, the tool implements a behaviour similar to IEEE 802.1CB (Frame Duplication),in which the packets to be transmitted are sent over all parallel links to increase reliability.Finally, different combinations of parameters are tested to see how they affect the link con-ditions, the number of links, and the possible retransmissions to the system’s behaviour.Moreover, use cases are exposed in which the tool could be useful to find the best con-figuration parameters to meet the requirements in terms of reliability and latency betweenpackets.

The most essential part of the Internet of Things (IoT) infrastructure is the wireless communication system that acts as a bridge for the delivery of data and control messages between the connected things and the Internet. Since the conception of the IoT, a large number of promising applications and technologies have been developed, which will change different aspects in our daily life. However, the existing wireless technologies lack the ability to support a huge amount of data exchange from many battery-driven devices, spread over a wide area. In order to support the IoT paradigm, IEEE 802.11ah is an Internet of Things enabling technology, where the efficient management of thousands of devices is a key function. This is one of the most promising and appealing standards, which aims to bridge the gap between traditional mobile networks and the demands of the IoT. To this aim, IEEE 802.11ah provides the Restricted Access Window (RAW) mechanism, which reduces contention by enabling transmissions for small groups of stations. Optimal grouping of RAW stations requires an evaluation of many possible configurations. In this thesis, we first discuss the main PHY and MAC layer amendments proposed for IEEE 802.11ah. Furthermore, we investigate the operability of IEEE 802.11ah as a backhaul link to connect devices over possibly long distances. Additionally, we compare the aforementioned standard with previous notable IEEE 802.11 amendments (i.e. IEEE 802.11n and IEEE 802.11ac) in terms of throughput (with and without frame aggregation) by utilizing the most robust modulation schemes. The results show an improved performance of IEEE 802.11ah (in terms of power received at long range while experiencing different packet error rates) as compared to previous IEEE 802.11 standards. Additionally, we expose the capabilities of future IEEE 802.11ah in supporting different IoT applications. In addition, we provide a brief overview of the technology contenders that are competing to cove, La clave del concepto Internet de las cosas (IoT) es que utiliza un sistema de comunicación inalámbrica, el cual actúa como puente para la entrega de datos y mensajes de control entre las "cosas" conectadas y el Internet. Desde la concepción del IoT, se han desarrollado gran cantidad de aplicaciones y tecnologías prometedoras que cambiarán distintos aspectos de nuestra vida diaria.Sin embargo, las tecnologías de redes computacionales inalámbricas existentes carecen de la capacidad de soportar las características del IoT, como las grandes cantidades de envío y recepción de datos desde múltiples dispositivos distribuidos en un área amplia, donde los dispositivos IoT funcionan con baterías. Para respaldar el paradigma del IoT, IEEE 802.11ah, la cual es una tecnología habilitadora del Internet de las cosas, para el cual la gestión eficiente de miles de dispositivos es una función clave. IEEE 802.11ah es uno de los estándares más prometedores y atractivos, desde su concepción orientada para IoT, su objetivo principal es cerrar la brecha entre las redes móviles tradicionales y la demandada por el IoT. Con este objetivo en mente, IEEE 802.11ah incluye entre sus características especificas el mecanismo de ventana de acceso restringido (RAW, por sus siglas en ingles), el cual define un nuevo período de acceso al canal libre de contención, reduciendo la misma al permitir transmisiones para pequeños grupos de estaciones. Nótese que para obtener una agrupación óptima de estaciones RAW, se requiere una evaluación de las distintas configuraciones posibles. En esta tesis, primero discutimos las principales mejoras de las capas PHY y MAC propuestas para IEEE 802.11ah. Además, investigamos la operatividad de IEEE 802.11ah como enlace de backhaul para conectar dispositivos a distancias largas. También, comparamos el estándar antes mencionado con las notables especificaciones IEEE 802.11 anteriores (es decir, IEEE 802.11n y IEEE 802.11ac), en términos de rendimiento (incluyendo y excl, Postprint (published version)

The evolution of wireless technologies has shifted the expectations on how sensors anddata collection is performed. By reducing the complexity to deploy and connect sensors, anincrease in the variety and quantity of data collected is leading to the concept of ubiquitoussensing.However, there are measurements that still rely on manual work. In particular, the auto-motive industry still performs manually the rain isolation quality tests. These tests requirenowadays for an employee to manually place a handheld moisture sensor device in con-tact with the area to analyse, taking long time, low scalability and prone to human errors.Additionally, workers are exposed to these humid conditions which is hazardous for theirhealth.In order to tackle this problem, low-cost wireless identification tags are proposed in thiswork to be used instead. These tags, known as Radio Frequency Identification (RFID),are bundled in pairs, one waterproofed and one exposed to work as moisture detectionsensor. By interacting with them with a RFID telecommunications system, low level datais collected to perform predictions about their status. This way, it is possible to measuresignal differences when subjected to different quantities of water exposure, both in staticand in-movement scenarios, which lead to predictive methods of water leaks.In this work, different methodologies and setups will be evaluated on their performanceas predictors of wet RFID tags. The project involves researching the state of the art onthis topic, empirical experimentation of the different proposed methods, development of anautomated software platform and potential contribution to the academia with the resultsfound, by means of paper publication in conferences and/or journals. In particular, twodifferent experimental setups were tested, a single antenna setup and dual antenna setup.These setups were implemented in a controlled scenario, where 48 different experimen-tal iterations were performed. Later, the setup was impl

Contribució a IEEE 802.11 Wireless Next Generation (WNG) Standing Committee (SC), Preprint

Its prominent position as non-line of sight RAT for data communications in small cells, along with its continuous evolution, make the IEEE 802.11 family of standards a good candidate to play a key role in future 5G deployments. Considering the latest advances in the physical layer (i.e. aggressive modulations up to 1024-QAM, MIMO, etc.), Wi-Fi is ready to offer throughput in the Gbps scale, which will surely grant it a place among the most widespread 5G RATs. The evolution towards 5G RAN brings new challenges in terms of time synchronisation requirements imposed to radio transmissions. To achieve such accuracy in the transport of timing, the expectation is to continue relying on a mix of centralised or distributed Global Navigation Satellite Service (GNSS) receivers networked towards the synchronisation clients through packet-based timing transport such as IEEE 1588 PTP. This has been the conventional approach due to inviable costs of deploying GNSS receivers in every base station, especially when considering indoor equipment.

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., Given the current evolution trends in mobile cellular networks, which is approaching us towards the future 5G paradigm, novel techniques for network management are in the agenda. Machine Learning techniques are useful for extracting knowledge out of raw data; knowledge that can be applied to improving the experience in the operation of such systems. This paper proposes the use of Machine Learning applied to energy efficiency, which is set to be one major challenge in future network deployments. By studying the cell-level traces collected in a real network, we can study traffic patterns and derive predictive models for different cell load metrics with the aid of different machine learning techniques. Such models are applied into a simulation environment designed to test different algorithms which, according to cell load predictions, dynamically switch on and off base stations with the aim of providing energy savings in a mobile cellular network., Postprint (author's final draft)

IEEE 802.11 is one of the most commonly used radio access technologies, being present in almost all handheld devices with networking capabilities. However, its energy-hungry communication modes are a challenge for the increased battery lifetime of such devices and are an obstacle for its use in battery-constrained devices such as the ones defined by many Internet of Things applications. Wake-up Radio (WuR) systems have appeared as a solution for increasing the energy efficiency of communication technologies by employing a secondary low-power radio interface, which is always in the active state and switches the primary transceiver (used for main data communication) from the energy-saving to the active operation mode. The high market penetration of IEEE 802.11 technology, together with the benefits that WuR systems can bring to this widespread technology, motivates this article’s focus on IEEE 802.11-basedWuR solutions. More specifically, we elaborate on the feasibility of such IEEE 802.11-based WuR solutions, and introduce the latest standardization efforts in this IEEE 802.11-based WuR domain, IEEE 802.11ba, which is a forthcoming IEEE 802.11 amendment, discussing its main features and potential use cases. As a use case consisting of green Wi-Fi application, we provide a proof-of-concept smart plug system implemented by a WuR that is activated remotely using IEEE 802.11 devices, evaluate its monetary and energy savings, and compare it with commercially available smart plug solutions. Finally, we discuss novel applications beyond the wake-up functionality that IEEE 802.11-enabled WuR devices can offer using a secondary radio, as well as applications that have not yet been considered by IEEE 802.11ba. As a result, we argue that the IEEE 802.11-based WuR solution will support a wide range of devices and deployments, for both low-rate and low-power communications, as well as high-rate transmissions., Postprint (author's final draft)

Adapting to recent trends in mobile communications towards 5G, infrastructure owners are gradually modifying their systems for supporting the network programmability paradigm and for participating in the slice market (i.e., dynamic leasing of virtual network slices to service providers). Two-fold are the advantages offered by this upgrade: i) enabling next generation services, and ii) allowing new profit opportunities. Many efforts exist already in the field of admission control, resource allocation and pricing for virtualized networks. Most of the 5G-related research efforts focus in technological enhancements for making existing solutions compliant to the strict requirements of next generation networks. On the other hand, the profit opportunities associated to the slice market also need to be reconsidered in order to assess the feasibility of this new business model. Nonetheless, when economic aspects are studied in the literature, technical constraints are generally oversimplified. For this reason, in this work, we propose an admission control mechanism for network slicing that respects 5G timeliness while maximizing network infrastructure providers' revenue, reducing expenditures and providing a fair slice provision to competing service providers. To this aim, we design an admission policy of reduced complexity based on bid selection, we study the optimal strategy in different circumstances (i.e., pool size of available resources, service providers' strategy and trafic load), analyze the performance metrics and compare the proposal against reference approaches. Finally, we explore the case where infrastructure providers lease network slices either on-demand or on a periodic time basis and provide a performance comparison between the two approaches. Our analysis shows that the proposed approach outperforms existing solutions, especially in the case of infrastructures with large pool of resources and under intense trafic conditions., Peer Reviewed, Postprint (published version)

Current standardization process for Wake-up Radio (WuR) within the IEEE 802.11 Working Group, known as the IEEE 802.11ba, has brought interest to the IEEE 802.11-related technologies for the implementation of WuR systems. This paper proposes a new WuR system, where the Wake-up Transmitter (WuTx) is based on the legacy IEEE 802.11 Orthogonal Frequency Division Modulation (OFDM) Physical Layer (PHY) specification. Using the IEEE 802.11, OFDM PHY makes it possible for an IEEE 802.11a/g/n/ac transmitter to operate as WuTx for this WuR system. The WuTx generates a Wake-up Signal (WuS) coded with an amplitude-based digital modulation, achieving a bit rate of 250 kbps. This modulation, which we call Peak-Flat modulation, can be received using low-power receivers. A simulated proof of concept of the WuTx based on the IEEE 802.11g is presented and evaluated using MATLAB WLAN Toolbox. A method to generate the Peak-Flat modulated WuS from an IEEE 802.11a/g standard-compliant transmitter, using only software-level access, is explained. In addition, two possible low-power Wake-up Receiver (WuRx) architectures capable of decoding the presented modulation are proposed. The design of those receivers is generic enough to be used as a reference to compare the performance of the Peak-Flat Modulation with the other state-of-the-art approaches. The evaluation results conclude that the Peak-Flat modulation has similar performance compared to the other IEEE 802.11 WuR solutions on the reference receivers. Moreover, this solution provides a notorious advantage: legacy OFDM-based IEEE 802.11 transmitters can generate the Peak-Flat modulated WuS., Postprint (published version)

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., Dense deployments of Small Cells are key to fulfill the capacity requirements of future 5G networks. However, two roadblocks to the adoption of Small Cells are i) the limited availability and the cost of sites with wired backhaul resources, and ii) the complexity to manage a dense deployment of wireless backhaul nodes. Towards these challenges we propose SODALITE, a novel system that applies Software Defined Networking (SDN) to a wireless backhaul network. We present how SODALITE can be integrated to 3GPP’s 4G and 5G architectures, and show the feasibility of SODALITE through LTE network testbed experiments. We substantiate the scalability of SODALITE through stochastic studies using real-life traffic traces from an LTE network and discuss the effects of cell densification and 5G system architecture on these studies. Further, a reliable backhauling solution for wireless links is introduced in SODALITE through SDN-enabled mechanisms that are capable of reconfiguring the data plane upon a link failure detection. Its reliability is shown through experiments on a LTE network testbed, and studied thoroughly via rigorous simulations and network emulator evaluations. As a result, we claim that SODALITE is a promising carrier-grade system to manage a wireless Small Cell backhaul., Postprint (author's final draft)

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., The new medium access method of IEEE 802.11ah, called Restricted Access Window (RAW), divides stations into different groups, and only allows stations in the same group to access the channel simultaneously, in order to reduce collisions and thus achieve better performance (e.g., throughput). However, the existing station grouping strategies only support homogeneous scenarios where all stations use the same modulation and coding scheme (MCS) and packet size. A surrogate model is an efficient mathematical model that represents the behavior of a complex system, trained with a limited set of labeled input-output data samples. In this paper, we present a surrogate model that can accurately predict RAW performance under a given Restricted Access Window (RAW) configuration in heterogeneous networks. Different from the homogeneous scenario, heterogeneous networks are defined by a large number of parameters, leading to an enormous design space, i.e., the order of 1017 possible data points. This is too big to achieve feasible training convergence. In this paper, we present a novel training methodology that leads to a new design space with highly reduced size, i.e., the order of 105 data points. The surrogate model converges when less than 6000 labeled data points are used for training, which is only a tiny portion of the whole design space. The results show that, the relative error between model prediction and simulation results is less than 0.1 for 95% of the data points, in the areas of the design space studied. Its low complexity and high precision make the proposed model a valuable tool to develop real-time RAW optimization algorithms for heterogeneous IEEE 802.11ah networks., Postprint (author's final draft)

The final publication is available at ACM via http://dx.doi.org/10.1145/3265863.3265881, Aggressive demand of future access network services is being translated into the stringent requirement on future backhaul infrastructure. It is not possible to take the backhaul resources for granted anymore; rather, more focused research is required to tackle the challenge of limited resources. It is also anticipated that, to meet the expectation of 5G, access and backhaul networks will work closely and therefore, total separation of their resources may not be possible anymore and joint operation is required. In this paper, we argue that, joint access-backhaul mechanisms is becoming necessary to ensure the best use of the scarce resources. We introduce the problem of statically assigning resources to capacity-limited backhaul links and we provide preliminary results to show the potential benefits of an intelligent access-aware backhaul capacity optimization scheme, where a central controller optimizes backhaul capacity according to corresponding access network requirements. Simulation results show that, with this approach, we are able to carry more traffic in a network limited by its backhaul capacity., Peer Reviewed, Postprint (published version)

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, 5G is coming with a promise to provide ubiquitous coverage with high data rate availability. To do so, densification of access points to enhance the system capacity is anticipated. For managing such densely populated network, 5G will be employing Centralized Radio Access Network (CRAN), where most of the Radio Access Network (RAN) functionalities are centralized in a central processing unit. This centralization reduces operational costs and eases implementation of advanced technologies, such as, Cooperative multipoint (CoMP) and enhanced inter-cell interference coordination (eICIC), in a cost efficient way. However, CRAN imposes stringent requirements on the fronthaul, i.e. the link connecting access points to the central unit, in terms of capacity and latency. Furthermore, future fronthaul networks are expected to rely on wireless technologies, since wired options are costly, not scalable and not always suitable for all scenarios. Therefore, meeting the expected requirements of fronthaul network utilizing capacity-limited wireless technologies may become an inescapable bottleneck. In this paper, we study different functional splits at the PHY layer in terms of data rate requirements and operational cost, and discuss the combination of different splits aimed at minimizing the overall cost and maximizing the centralization gains, while keeping the capacity requirements below the limit of the fronthaul., Peer Reviewed, Postprint (published version)