Leveraging 6G Technologies to Optimize Information Freshness for Time-Sensitive Applications

Next-generation wireless networks (Beyond 5G, 6G) aim to provide tremendous improvements over previous generations by promising a massive connectivity, ultra-reliable and low-latency communications, and soaring broadband speeds. Such transformation will give rise to a wide range of propitious Internet-of-Things (IoT) applications such as intelligent transportation systems (ITS), tactile internet, augmented/virtual reality, industry 4.0, etc. These applications possess stringent requirements of fresh and timely information updates to make critical decisions. Out-dated or stale information updates are highly undesirable for these applications as they may call forth unreliable or erroneous decisions. The conventional performance metrics such as delay and latency may not fully characterize the freshness of information for time-critical IoT applications. Recently, information freshness has been investigated through defining a new performance metric termed as Age of Information (AoI). AoI offers a rigorous way to quantify the information freshness as compared to other performance metrics and is deemed suitable for real-time IoT applications. In reality, the limited energy and computing resources of IoT devices (IoTDs) is a significant challenge towards realizing the timely delivery of information updates. To address this challenge, the first aim of this dissertation is to examine the capability of multi-access edge computing (MEC) towards minimizing the AoI. In fact, MEC offers an expedited computation of resource-intensive tasks, which, if processed locally at the IoTDs, may experience excessive computational latency. In this context, an optimization problem is setup to determine the optimal scheduling policy with the goal of minimizing the expected sum AoI of multiple IoTDs, while considering the combined impact of unreliable channel conditions and random packet arrivals. Another acute challenge is the high randomness and uncontrollable behaviour of wireless communication environments, which may severely impede the timely and reliable delivery of information updates. Towards addressing this challenge, reconfigurable intelligent surface (RIS) is leveraged to mitigate the propagation-induced impairments of the wireless environment and enhance the quality of wireless links to preserve the information freshness. First, a wireless network consisting of a base station (BS) that is forwarding information updates of multiple real-time traffic streams to their destinations is studied. The considered multiple access technique is frequency division multiple access (FDMA), which is an orthogonal multiple access (OMA) technique. A joint user scheduling and phase-shift matrix (passive beamforming) optimization problem is formulated with the objective of minimizing the expected sum AoI of the coexisting multiple traffic streams. The resulting problem is a mixed integer non-convex optimization problem. To evade the high coupling of the invoked optimization variables, the bi-level optimization technique is utilized, where the original problem is decomposed into an outer traffic stream scheduling problem and an inner RIS phase-shift matrix problem. Owing to the stochastic nature of packet arrivals, a deep reinforcement learning (DRL) solution is employed to solve the outer problem. To do so, the traffic stream scheduling is modeled as a Markov Decision Process (MDP) and Proximal Policy Optimization (PPO) is invoked to solve it. On the other hand, the inner problem that determines the RIS configuration is solved through semi-definite relaxation (SDR). Due to the limitations of OMA techniques in terms of the number of served IoTDs and the spectral efficiency, the focus of this dissertation shifts to explore non-orthogonal multiple access (NOMA) scheme towards achieving the goal of minimizing the AoI in an uplink setting. In this context, an optimization problem is formulated to optimize the RIS configuration, the transmit power of IoTDs and their clustering policy. To solve this mixed-integer non-convex problem, the RIS configuration is obtained first by resorting to difference-of-convex (DC) along with successive convex approximation (SCA). On the other hand, the bi-level optimization is used to solve the power allocation and the clustering problems. Optimal closed-form expressions are derived for the power control scheme and the one-to-one matching is employed to solve the clustering problem. Aiming to further improve the information freshness in time-critical IoT applications, an extended version of NOMA, termed as Cooperative-NOMA (C-NOMA), is adopted. In C-NOMA, the cooperation between IoTDs through device-to-device (D2D) communication and full-duplex (FD) relaying is invoked within the NOMA scheme. In this context, the integration of RIS and C-NOMA is investigated towards achieving the goal of minimizing the average sum AoI. Precisely, it is investigated how much performance gain in terms of AoI reduction can be brought by the RIS-enabled uplink C-NOMA system compared to the conventional C-NOMA and NOMA schemes, both with and without RIS. Results elucidate the superiority of our proposed approaches against other baseline schemes. The findings in this dissertation shed light on the choice of effective design of wireless communication networks leveraging the core future enabling technologies

PoPeC: PAoI-Centric Task Offloading with Priority over Unreliable Channels

Freshness-aware computation offloading has garnered great attention recently in the edge computing arena, with the aim of promptly obtaining up-to-date information and minimizing the transmission of outdated data. However, most of the existing work assumes that wireless channels are reliable and neglect the dynamics and stochasticity thereof. In addition, varying priorities of offloading tasks along with heterogeneous computing units also pose significant challenges in effective task scheduling and resource allocation. To address these challenges, we cast the freshness-aware task offloading problem as a multi-priority optimization problem, considering the unreliability of wireless channels, the heterogeneity of edge servers, and prioritized users. Based on the nonlinear fractional programming and ADMM-Consensus method, we propose a joint resource allocation and task offloading algorithm to solve the original problem iteratively. To improve communication efficiency, we further devise a distributed asynchronous variant for the proposed algorithm. We rigorously analyze the performance and convergence of the proposed algorithms and conduct extensive simulations to corroborate their efficacy and superiority over the existing baselines

Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems

Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of the vehicle networks, it is rather challenging to make timely and accurate decisions of vehicle behaviors. Moreover, in the presence of mobile wireless communications, the privacy and security of vehicle information are at constant risk. In this context, a new paradigm is urgently needed for various applications in dynamic vehicle environments. As a distributed machine learning technology, federated learning (FL) has received extensive attention due to its outstanding privacy protection properties and easy scalability. We conduct a comprehensive survey of the latest developments in FL for ITS. Specifically, we initially research the prevalent challenges in ITS and elucidate the motivations for applying FL from various perspectives. Subsequently, we review existing deployments of FL in ITS across various scenarios, and discuss specific potential issues in object recognition, traffic management, and service providing scenarios. Furthermore, we conduct a further analysis of the new challenges introduced by FL deployment and the inherent limitations that FL alone cannot fully address, including uneven data distribution, limited storage and computing power, and potential privacy and security concerns. We then examine the existing collaborative technologies that can help mitigate these challenges. Lastly, we discuss the open challenges that remain to be addressed in applying FL in ITS and propose several future research directions

Latency and Reliability Aware Edge Computation Offloading in 5G Networks

Empowered by recent technological advances and driven by the ever-growing population density and needs, the conception of 5G has opened up the expectations of what mobile networks are capable of to heights never seen before, promising to unleash a myriad of new business practices and paving the way for a surging number of user equipments to carry out novel service operations. The advent of 5G and networks beyond will hence enable the vision of Internet of Things (IoT) and smart city with its ubiquitous and heterogeneous use cases belonging to various verticals operating on a common underlying infrastructure, such as smart healthcare, autonomous driving, and smart manufacturing, while imposing extreme unprecedented Quality of Service (QoS) requirements in terms of latency and reliability among others. Due to the necessity of those modern services such as traffic coordination, industrial processes, and mission critical applications to perform heavy workload computations on the collected input, IoT devices such as cameras, sensors, and Cyber-Physical Systems (CPSs), which have limited energy and processing capabilities are put under an unusual strain to seamlessly carry out the required service computations. While offloading the devices' workload to cloud data centers with Mobile Cloud Computing (MCC) remains a possible alternative which also brings about a high computation reliability, the latency incurred from this approach would prevent from satisfying the services' QoS requirements, in addition to elevating the load in the network core and backhaul, rendering MCC an inadequate solution for handling the 5G services' required computations. In light of this development, Multi-access Edge Computing (MEC) has been proposed as a cutting edge technology for realizing a low-latency computation offloading by bringing the cloud to the vicinity of end-user devices as processing units co-located within base stations leveraging the virtualization technique. Although it promises to satisfy the stringent latency service requirements, realizing the edge-cloud solution is coupled with various challenges, such as the edge servers' restricted capacity, their reduced processing reliability, the IoT devices' limited offloading energy, the wireless offloading channels' often weak quality, the difficulty to adapt to dynamic environment changes and to under-served networks, and the Network Operators (NOs)' cost-efficiency concerns. In light of those conditions, the NOs are consequently looking to devise efficient innovative computation offloading schemes through leveraging novel technologies and architectures for guaranteeing the seamless provisioning of modern services with their stringent latency and reliability QoS requirements, while ensuring the effective utilization of the various network and devices' available resources. Leveraging a hierarchical arrangement of MEC with second-tier edge servers co-located within aggregation nodes and macro-cells can expand the edge network's capability, while utilizing Unmanned Aerial Vehicles (UAVs) to provision the MEC service via UAV-mounted cloudlets can increase the availability, flexibility, and scalability of the computation offloading solution. Moreover, aiding the MEC system with UAVs and Intelligent Reflecting Surfaces (IRSs) can improve the computation offloading performance by enhancing the wireless communication channels' conditions. By effectively leveraging those novel technologies while tackling their challenges, the edge-cloud paradigm will bring about a tremendous advancement to 5G networks and beyond, opening the door to enabling all sorts of modern and futuristic services. In this dissertation, we attempt to address key challenges linked to realizing the vision of a low-latency and high-reliability edge computation offloading in modern networks while exploring the aid of multiple 5G network technologies. Towards that end, we provide novel contributions related to the allocation of network and devices' resources as well as the optimization of other offloading parameters, and thereby efficiently utilizing the underlying infrastructure such as to enable energy and cost-efficient computation offloading schemes, by leveraging several customized solutions and optimization techniques. In particular, we first tackle the computation offloading problem considering a multi-tier MEC with a deployed second-tier edge-cloud, where we optimize its use through proposed low-complexity algorithms, such as to achieve an energy and cost-efficient solution that guarantees the services' latency requirements. Due to the significant advantage of operating MEC in heterogeneous networks, we extend the scenario to a network of small-cells with the second-tier edge server being co-located within the macro-cell which can be reached through a wireless backhaul, where we optimize the macro-cell server use along with the other offloading parameters through a proposed customized algorithm based on the Successive Convex Approximation (SCA) technique. Then, given the UAVs' considerable ability in expanding the capabilities of cellular networks and MEC systems, we study the latency and reliability aware optimized positioning and use of UAV-mounted cloudlets for computation offloading through two planning and operational problems while considering tasks redundancy, and propose customized solutions for solving those problems. Finally, given the IRSs' ability to also enhance the channel conditions through the tuning of their passive reflecting elements, we extend the latency and reliability aware study to a scenario of an IRS-aided MEC system considering both a single-user and multi-user OFDMA cases, where we explore the optimized IRSs' use in order to reveal their role in reducing the UEs' offloading consumption energy and saving the network resources, through proposed customized solutions based on the SCA approach and the SDR technique

Multi-criteria decision support for energy-efficient IoT edge computing offloading

Computation offloading is one of the primary technological enablers of the Internet of Things (IoT), as it helps address individual devices’ resource restrictions (e.g. process- ing and memory). In the past, offloading would always utilise remote cloud infrastruc- tures, but the increasing size of IoT data traffic and the real-time response requirements of modern and future IoT applications have led to the adoption of the edge computing paradigm, where the data is processed at the edge of the network, closer to the IoT devices. The decision as to whether cloud or edge resources will be utilised is typically taken at the design stage, based on the type of the IoT device. Yet, the conditions that determine the optimality of this decision, such as the arrival rate, nature and sizes of the tasks, and crucially the real-time conditions of the networks involved, keep changing. At the same time, the energy consumption of IoT devices is usually a key requirement, which is affected primarily by the time it takes to complete tasks, whether for the actual computation or for offloading them through the network. This thesis presents a dynamic computation offloading mechanism, which improves the performance (i.e. in terms of response time) and energy consumption of IoT de- vices in a decentralised and autonomous manner. We initially propose the Multi-critEria DecIsion support meChanism for IoT offloading(MEDICI), which runs independently on an IoT device, enabling it to make offloading decisions dynamically, based on multiple criteria, such as the state of the IoT, edge or cloud devices and the conditions of the net- work connecting them. It provides mathematical models of the expected time and energy costs for the different options of offloading a task (i.e. to the edge or the cloud or the IoT device itself). To evaluate its effectiveness, we provide simulation results, by extending the EdgeCloudSim simulator, comparing it against previous families of approaches used in the literature. Our simulations on four different types of IoT applications show that allowing customisation and dynamic offloading decision support can improve drastically the response time of time-critical and small-size applications, such as IoT cyber intrusion detection, and the energy consumption not only of the individual IoT devices but also of the system as a whole. Furthermore, we present an enhancement of our MEDICI mechanism, the ProbeLess Multi-critEria DecIsion support meChanism for IoT offloading (PL-MEDICI), which en- ables MEDICI to operate in real IoT environments without the need for probing or having pre-defined parameters in order to estimate or model the network conditions or the com- putation capabilities of the different devices involved. This is the first probeless dynamic and decentralised offloading decision support mechanism for IoT environments. The probeless property is achieved by combining lightweight statistical techniques with the concept of age of knowledge (AoK) to allow us to have accurate enough information to use for our estimations. We provide experimental results performed in a real IoT testbed with three real IoT applications, showcasing that PL-MEDICI outperforms existing techniques in terms of both response time and energy consumption. Finally, in order to further evaluate our PL-MEDICI mechanism, we formulate a mixed- integer linear program optimisation problem that provides the theoretical optimal cen- tralised solution to our problem. This is used to compare our PL-MEDICI against the theoretical optimum, given the same estimated input. Our results showed that our of- floading mechanism is close to the obtained optimal solution in terms of both the re- sponse time and energy consumptio

Enhanced mobility management mechanisms for 5G networks

Many mechanisms that served the legacy networks till now, are being identified as being grossly sub-optimal for 5G networks. The reason being, the increased complexity of the 5G networks compared previous legacy systems. One such class of mechanisms, important for any wireless standard, is the Mobility Management (MM) mechanisms. MM mechanismsensure the seamless connectivity and continuity of service for a user when it moves away from the geographic location where it initially got attached to the network. In this thesis, we firstly present a detailed state of the art on MM mechanisms. Based on the 5G requirements as well as the initial discussions on Beyond 5G networks, we provision a gap analysis for the current technologies/solutions to satisfy the presented requirements. We also define the persistent challenges that exist concerning MM mechanisms for 5G and beyond networks. Based on these challenges, we define the potential solutions and a novel framework for the 5G and beyond MM mechanisms. This framework specifies a set of MM mechanisms at the access, core and the extreme edge network (users/devices) level, that will help to satisfy the requirements for the 5G and beyond MM mechanisms. Following this, we present an on demand MM service concept. Such an on-demand feature provisions the necessary reliability, scalability and flexibility to the MM mechanisms. It's objective is to ensure that appropriate resources and mobility contexts are defined for users who will have heterogeneous mobility profiles, versatile QoS requirements in a multi-RAT network. Next, in this thesis we tackle the problem of core network signaling that occurs during MM in 5G/4G networks. A novel handover signaling mechanism has been developed, which eliminates unnecessary handshakes during the handover preparation phase, while allowing the transition to future softwarized network architectures. We also provide a handover failure aware handover preparation phase signaling process. We then utilize operator data and a realistic network deployment to perform a comparative analysis of the proposed strategy and the 3GPP handover signaling strategy on a network wide deployment scenario. We show the benefits of our strategy in terms of latency of handover process, and the transmission and processing cost incurred. Lastly, a novel user association and resource allocation methodology, namely AURA-5G, has been proposed. AURA-5G addresses scenarios wherein applications with heterogeneous requirements, i.e., enhanced Mobile Broadband (eMBB) and massive Machine Type Communications (mMTC), are present simultaneously. Consequently, a joint optimization process for performing the user association and resource allocation while being cognizant of heterogeneous application requirements, has been performed. We capture the peculiarities of this important mobility management process through the various constraints, such as backhaul requirements, dual connectivity options, available access resources, minimum rate requirements, etc., that we have imposed on a Mixed Integer Linear Program (MILP). The objective function of this established MILP problem is to maximize the total network throughput of the eMBB users, while satisfying the minimum requirements of the mMTC and eMBB users defined in a given scenario. Through numerical evaluations we show that our approach outperforms the baseline user association scenario significantly. Moreover, we have presented a system fairness analysis, as well as a novel fidelity and computational complexity analysis for the same, which express the utility of our methodology given the myriad network scenarios.Muchos mecanismos que sirvieron en las redes actuales, se están identificando como extremadamente subóptimos para las redes 5G. Esto es debido a la mayor complejidad de las redes 5G. Un tipo de mecanismo importante para cualquier estándar inalámbrico, consiste en el mecanismo de gestión de la movilidad (MM). Los mecanismos MM aseguran la conectividad sin interrupciones y la continuidad del servicio para un usuario cuando éste se aleja de la ubicación geográfica donde inicialmente se conectó a la red. En esta tesis, presentamos, en primer lugar, un estado del arte detallado de los mecanismos MM. Bas ándonos en los requisitos de 5G, así como en las discusiones iniciales sobre las redes Beyond 5G, proporcionamos un análisis de las tecnologías/soluciones actuales para satisfacer los requisitos presentados. También definimos los desafíos persistentes que existen con respecto a los mecanismos MM para redes 5G y Beyond 5G. En base a estos desafíos, definimos las posibles soluciones y un marco novedoso para los mecanismos 5G y Beyond 5G de MM. Este marco especifica un conjunto de mecanismos MM a nivel de red acceso, red del núcleo y extremo de la red (usuarios/dispositivos), que ayudarán a satisfacer los requisitos para los mecanismos MM 5G y posteriores. A continuación, presentamos el concepto de servicio bajo demanda MM. Tal característica proporciona la confiabilidad, escalabilidad y flexibilidad necesarias para los mecanismos MM. Su objetivo es garantizar que se definan los recursos y contextos de movilidad adecuados para los usuarios que tendrán perfiles de movilidad heterogéneos, y requisitos de QoS versátiles en una red multi-RAT. Más adelante, abordamos el problema de la señalización de la red troncal que ocurre durante la gestión de la movilidad en redes 5G/4G. Se ha desarrollado un nuevo mecanismo de señalización de handover, que elimina los intercambios de mensajes innecesarios durante la fase de preparación del handover, al tiempo que permite la transición a futuras arquitecturas de red softwarizada. Utilizamos los datos de operadores y consideramos un despliegue de red realista para realizar un análisis comparativo de la estrategia propuesta y la estrategia de señalización de 3GPP. Mostramos los beneficios de nuestra estrategia en términos de latencia del proceso de handover y los costes de transmisión y procesado. Por último, se ha propuesto una nueva asociación de usuarios y una metodología de asignación de recursos, i.e, AURA-5G. AURA-5G aborda escenarios en los que las aplicaciones con requisitos heterogéneos, i.e., enhanced Mobile Broadband (eMBB) y massive Machine Type Communications (mMTC), están presentes simultáneamente. En consecuencia, se ha llevado a cabo un proceso de optimización conjunta para realizar la asociación de usuarios y la asignación de recursos mientras se tienen en cuenta los requisitos de aplicaciónes heterogéneas. Capturamos las peculiaridades de este importante proceso de gestión de la movilidad a través de las diversas restricciones impuestas, como son los requisitos de backhaul, las opciones de conectividad dual, los recursos de la red de acceso disponibles, los requisitos de velocidad mínima, etc., que hemos introducido en un Mixed Integer Linear Program (MILP). La función objetivo de este problema MILP es maximizar el rendimiento total de la red de los usuarios de eMBB, y a la vez satisfacer los requisitos mínimos de los usuarios de mMTC y eMBB definidos en un escenario dado. A través de evaluaciones numéricas, mostramos que nuestro enfoque supera significativamente el escenario de asociación de usuarios de referencia. Además, hemos presentado un análisis de la justicia del sistema, así como un novedoso análisis de fidelidad y complejidad computacional para el mismo, que expresa la utilidad de nuestra metodología

Enhanced mobility management mechanisms for 5G networks

Many mechanisms that served the legacy networks till now, are being identified as being grossly sub-optimal for 5G networks. The reason being, the increased complexity of the 5G networks compared previous legacy systems. One such class of mechanisms, important for any wireless standard, is the Mobility Management (MM) mechanisms. MM mechanismsensure the seamless connectivity and continuity of service for a user when it moves away from the geographic location where it initially got attached to the network. In this thesis, we firstly present a detailed state of the art on MM mechanisms. Based on the 5G requirements as well as the initial discussions on Beyond 5G networks, we provision a gap analysis for the current technologies/solutions to satisfy the presented requirements. We also define the persistent challenges that exist concerning MM mechanisms for 5G and beyond networks. Based on these challenges, we define the potential solutions and a novel framework for the 5G and beyond MM mechanisms. This framework specifies a set of MM mechanisms at the access, core and the extreme edge network (users/devices) level, that will help to satisfy the requirements for the 5G and beyond MM mechanisms. Following this, we present an on demand MM service concept. Such an on-demand feature provisions the necessary reliability, scalability and flexibility to the MM mechanisms. It's objective is to ensure that appropriate resources and mobility contexts are defined for users who will have heterogeneous mobility profiles, versatile QoS requirements in a multi-RAT network. Next, in this thesis we tackle the problem of core network signaling that occurs during MM in 5G/4G networks. A novel handover signaling mechanism has been developed, which eliminates unnecessary handshakes during the handover preparation phase, while allowing the transition to future softwarized network architectures. We also provide a handover failure aware handover preparation phase signaling process. We then utilize operator data and a realistic network deployment to perform a comparative analysis of the proposed strategy and the 3GPP handover signaling strategy on a network wide deployment scenario. We show the benefits of our strategy in terms of latency of handover process, and the transmission and processing cost incurred. Lastly, a novel user association and resource allocation methodology, namely AURA-5G, has been proposed. AURA-5G addresses scenarios wherein applications with heterogeneous requirements, i.e., enhanced Mobile Broadband (eMBB) and massive Machine Type Communications (mMTC), are present simultaneously. Consequently, a joint optimization process for performing the user association and resource allocation while being cognizant of heterogeneous application requirements, has been performed. We capture the peculiarities of this important mobility management process through the various constraints, such as backhaul requirements, dual connectivity options, available access resources, minimum rate requirements, etc., that we have imposed on a Mixed Integer Linear Program (MILP). The objective function of this established MILP problem is to maximize the total network throughput of the eMBB users, while satisfying the minimum requirements of the mMTC and eMBB users defined in a given scenario. Through numerical evaluations we show that our approach outperforms the baseline user association scenario significantly. Moreover, we have presented a system fairness analysis, as well as a novel fidelity and computational complexity analysis for the same, which express the utility of our methodology given the myriad network scenarios.Muchos mecanismos que sirvieron en las redes actuales, se están identificando como extremadamente subóptimos para las redes 5G. Esto es debido a la mayor complejidad de las redes 5G. Un tipo de mecanismo importante para cualquier estándar inalámbrico, consiste en el mecanismo de gestión de la movilidad (MM). Los mecanismos MM aseguran la conectividad sin interrupciones y la continuidad del servicio para un usuario cuando éste se aleja de la ubicación geográfica donde inicialmente se conectó a la red. En esta tesis, presentamos, en primer lugar, un estado del arte detallado de los mecanismos MM. Bas ándonos en los requisitos de 5G, así como en las discusiones iniciales sobre las redes Beyond 5G, proporcionamos un análisis de las tecnologías/soluciones actuales para satisfacer los requisitos presentados. También definimos los desafíos persistentes que existen con respecto a los mecanismos MM para redes 5G y Beyond 5G. En base a estos desafíos, definimos las posibles soluciones y un marco novedoso para los mecanismos 5G y Beyond 5G de MM. Este marco especifica un conjunto de mecanismos MM a nivel de red acceso, red del núcleo y extremo de la red (usuarios/dispositivos), que ayudarán a satisfacer los requisitos para los mecanismos MM 5G y posteriores. A continuación, presentamos el concepto de servicio bajo demanda MM. Tal característica proporciona la confiabilidad, escalabilidad y flexibilidad necesarias para los mecanismos MM. Su objetivo es garantizar que se definan los recursos y contextos de movilidad adecuados para los usuarios que tendrán perfiles de movilidad heterogéneos, y requisitos de QoS versátiles en una red multi-RAT. Más adelante, abordamos el problema de la señalización de la red troncal que ocurre durante la gestión de la movilidad en redes 5G/4G. Se ha desarrollado un nuevo mecanismo de señalización de handover, que elimina los intercambios de mensajes innecesarios durante la fase de preparación del handover, al tiempo que permite la transición a futuras arquitecturas de red softwarizada. Utilizamos los datos de operadores y consideramos un despliegue de red realista para realizar un análisis comparativo de la estrategia propuesta y la estrategia de señalización de 3GPP. Mostramos los beneficios de nuestra estrategia en términos de latencia del proceso de handover y los costes de transmisión y procesado. Por último, se ha propuesto una nueva asociación de usuarios y una metodología de asignación de recursos, i.e, AURA-5G. AURA-5G aborda escenarios en los que las aplicaciones con requisitos heterogéneos, i.e., enhanced Mobile Broadband (eMBB) y massive Machine Type Communications (mMTC), están presentes simultáneamente. En consecuencia, se ha llevado a cabo un proceso de optimización conjunta para realizar la asociación de usuarios y la asignación de recursos mientras se tienen en cuenta los requisitos de aplicaciónes heterogéneas. Capturamos las peculiaridades de este importante proceso de gestión de la movilidad a través de las diversas restricciones impuestas, como son los requisitos de backhaul, las opciones de conectividad dual, los recursos de la red de acceso disponibles, los requisitos de velocidad mínima, etc., que hemos introducido en un Mixed Integer Linear Program (MILP). La función objetivo de este problema MILP es maximizar el rendimiento total de la red de los usuarios de eMBB, y a la vez satisfacer los requisitos mínimos de los usuarios de mMTC y eMBB definidos en un escenario dado. A través de evaluaciones numéricas, mostramos que nuestro enfoque supera significativamente el escenario de asociación de usuarios de referencia. Además, hemos presentado un análisis de la justicia del sistema, así como un novedoso análisis de fidelidad y complejidad computacional para el mismo, que expresa la utilidad de nuestra metodología.Postprint (published version

Recent Advances in Machine Learning for Network Automation in the O-RAN

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation using ML in O-RAN. We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support for ML techniques. The survey then explores challenges in network automation using ML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects where ML techniques can benefit.Peer reviewe

Internet of Things and Sensors Networks in 5G Wireless Communications

The Internet of Things (IoT) has attracted much attention from society, industry and academia as a promising technology that can enhance day to day activities, and the creation of new business models, products and services, and serve as a broad source of research topics and ideas. A future digital society is envisioned, composed of numerous wireless connected sensors and devices. Driven by huge demand, the massive IoT (mIoT) or massive machine type communication (mMTC) has been identified as one of the three main communication scenarios for 5G. In addition to connectivity, computing and storage and data management are also long-standing issues for low-cost devices and sensors. The book is a collection of outstanding technical research and industrial papers covering new research results, with a wide range of features within the 5G-and-beyond framework. It provides a range of discussions of the major research challenges and achievements within this topic