A Comprehensive Survey of the Tactile Internet: State of the art and Research Directions

The Internet has made several giant leaps over the years, from a fixed to a mobile Internet, then to the Internet of Things, and now to a Tactile Internet. The Tactile Internet goes far beyond data, audio and video delivery over fixed and mobile networks, and even beyond allowing communication and collaboration among things. It is expected to enable haptic communication and allow skill set delivery over networks. Some examples of potential applications are tele-surgery, vehicle fleets, augmented reality and industrial process automation. Several papers already cover many of the Tactile Internet-related concepts and technologies, such as haptic codecs, applications, and supporting technologies. However, none of them offers a comprehensive survey of the Tactile Internet, including its architectures and algorithms. Furthermore, none of them provides a systematic and critical review of the existing solutions. To address these lacunae, we provide a comprehensive survey of the architectures and algorithms proposed to date for the Tactile Internet. In addition, we critically review them using a well-defined set of requirements and discuss some of the lessons learned as well as the most promising research directions

Traffic control for energy harvesting virtual small cells via reinforcement learning

Due to the rapid growth of mobile data traffic, future mobile networks are expected to support at least 1000 times more capacity than 4G systems. This trend leads to an increasing energy demand from mobile networks which raises both economic and environmental concerns. Energy costs are becoming an important part of OPEX by Mobile Network Operators (MNOs). As a result, the shift towards energy-oriented design and operation of 5G and beyond systems has been emphasized by academia, industries as well as standard bodies. In particular, Radio Access Network (RAN) is the major energy consuming part of cellular networks. To increase the RAN efficiency, Cloud Radio Access Network (CRAN) has been proposed to enable centralized cloud processing of baseband functions while Base Stations (BSs) are reduced to simple Radio Remote Heads (RRHs). The connection between the RRHs and central cloud is provided by high capacity and very low latency fronthaul. Flexible functional splits between local BS sites and a central cloud are then proposed to relax the CRAN fronthaul requirements via partial processing of baseband functions at the local BS sites. Moreover, Network Function Virtualization (NFV) and Software Defined Networking (SDN) enable flexibility in placement and control of network functions. Relying on SDN/NFV with flexible functional splits, network functions of small BSs can be virtualized and placed at different sites of the network. These small BSs are known as virtual Small Cells (vSCs). More recently, Multi-access Edge Computing (MEC) has been introduced where BSs can leverage cloud computing capabilities and offer computational resources on demand basis. On the other hand, Energy Harvesting (EH) is a promising technology ensuring both cost effectiveness and carbon footprint reduction. However, EH comes with challenges mainly due to intermittent and unreliable energy sources. In EH Base Stations (EHBSs), it is important to intelligently manage the harvested energy as well as to ensure energy storage provision. Consequently, MEC enabled EHBSs can open a new frontier in energy-aware processing and sharing of processing units according to flexible functional split options. The goal of this PhD thesis is to propose energy-aware control algorithms in EH powered vSCs for efficient utilization of harvested energy and lowering the grid energy consumption of RAN, which is the most power consuming part of the network. We leverage on virtualization and MEC technologies for dynamic provision of computational resources according to functional split options employed by the vSCs. After describing the state-of-the-art, the first part of the thesis focuses on offline optimization for efficient harvested energy utilization via dynamic functional split control in vSCs powered by EH. For this purpose, dynamic programming is applied to determine the performance bound and comparison is drawn against static configurations. The second part of the thesis focuses on online control methods where reinforcement learning based controllers are designed and evaluated. In particular, more focus is given towards the design of multi-agent reinforcement learning to overcome the limitations of centralized approaches due to complexity and scalability. Both tabular and deep reinforcement learning algorithms are tailored in a distributed architecture with emphasis on enabling coordination among the agents. Policy comparison among the online controllers and against the offline bound as well as energy and cost saving benefits are also analyzed.Debido al rápido crecimiento del tráfico de datos móviles, se espera que las redes móviles futuras admitan al menos 1000 veces más capacidad que los sistemas 4G. Esta tendencia lleva a una creciente demanda de energía de las redes móviles, lo que plantea preocupaciones económicas y ambientales. Los costos de energía se están convirtiendo en una parte importante de OPEX por parte de los operadores de redes móviles (MNO). Como resultado, la academia, las industrias y los organismos estándar han enfatizado el cambio hacia el diseño orientado a la energía y la operación de sistemas 5G y más allá de los sistemas. En particular, la red de acceso por radio (RAN) es la principal parte de las redes celulares que consume energía. Para aumentar la eficiencia de la RAN, se ha propuesto Cloud Radio Access Network (CRAN) para permitir el procesamiento centralizado en la nube de las funciones de banda base, mientras que las estaciones base (BS) se reducen a simples cabezales remotos de radio (RRH). La conexión entre los RRHs y la nube central es proporcionada por una capacidad frontal de muy alta latencia y muy baja latencia. Luego se proponen divisiones funcionales flexibles entre los sitios de BS locales y una nube central para relajar los requisitos de red de enlace CRAN a través del procesamiento parcial de las funciones de banda base en los sitios de BS locales. Además, la virtualización de funciones de red (NFV) y las redes definidas por software (SDN) permiten flexibilidad en la colocación y el control de las funciones de red. Confiando en SDN / NFV con divisiones funcionales flexibles, las funciones de red de pequeñas BS pueden virtualizarse y ubicarse en diferentes sitios de la red. Estas pequeñas BS se conocen como pequeñas celdas virtuales (vSC). Más recientemente, se introdujo la computación perimetral de acceso múltiple (MEC) donde los BS pueden aprovechar las capacidades de computación en la nube y ofrecer recursos computacionales según la demanda. Por otro lado, Energy Harvesting (EH) es una tecnología prometedora que garantiza tanto la rentabilidad como la reducción de la huella de carbono. Sin embargo, EH presenta desafíos principalmente debido a fuentes de energía intermitentes y poco confiables. En las estaciones base EH (EHBS), es importante administrar de manera inteligente la energía cosechada, así como garantizar el suministro de almacenamiento de energía. En consecuencia, los EHBS habilitados para MEC pueden abrir una nueva frontera en el procesamiento con conciencia energética y el intercambio de unidades de procesamiento de acuerdo con las opciones de división funcional flexible. El objetivo de esta tesis doctoral es proponer algoritmos de control conscientes de la energía en vSC alimentados por EH para la utilización eficiente de la energía cosechada y reducir el consumo de energía de la red de RAN, que es la parte más consumidora de la red. Aprovechamos las tecnologías de virtualización y MEC para la provisión dinámica de recursos computacionales de acuerdo con las opciones de división funcional empleadas por los vSC. La primera parte de la tesis se centra en la optimización fuera de línea para la utilización eficiente de la energía cosechada a través del control dinámico de división funcional en vSC con tecnología EH. Para este propósito, la programación dinámica se aplica para determinar el rendimiento limitado y la comparación se realiza con configuraciones estáticas. La segunda parte de la tesis se centra en los métodos de control en línea donde se diseñan y evalúan los controladores basados en el aprendizaje por refuerzo. En particular, se presta más atención al diseño de aprendizaje de refuerzo de múltiples agentes para superar las limitaciones de los enfoques centralizados debido a la complejidad y la escalabilidad. También se analiza la comparación de políticas entre los controladores en línea y contra los límites fuera de línea,Postprint (published version

Transforming Large-Scale Virtualized Networks: Advancements in Latency Reduction, Availability Enhancement, and Security Fortification

In today’s digital age, the increasing demand for networks, driven by the proliferation of connected devices, data-intensive applications, and transformative technologies, necessitates robust and efficient network infrastructure. This thesis addresses the challenges posed by virtualization in 5G networking and focuses on enhancing next-generation Radio Access Networks (RANs), particularly Open-RAN (O-RAN). The objective is to transform virtualized networks into highly reliable, secure, and latency-aware systems. To achieve this, the thesis proposes novel strategies for virtual function placement, traffic steering, and virtual function security within O-RAN. These solutions utilize optimization techniques such as binary integer programming, mixed integer binary programming, column generation, and machine learning algorithms, including supervised learning and deep reinforcement learning. By implementing these contributions, network service providers can deploy O-RAN with enhanced reliability, speed, and security, specifically tailored for Ultra-Reliable and Low Latency Communications use cases. The optimized RAN virtualization achieved through this research unlocks a new era in network architecture that can confidently support URLLC applications, including Autonomous Vehicles, Industrial Automation and Robotics, Public Safety and Emergency Services, and Smart Grids

View on 5G Architecture: Version 2.0

The 5G Architecture Working Group as part of the 5GPPP Initiative is looking at capturing novel trends and key technological enablers for the realization of the 5G architecture. It also targets at presenting in a harmonized way the architectural concepts developed in various projects and initiatives (not limited to 5GPPP projects only) so as to provide a consolidated view on the technical directions for the architecture design in the 5G era. The first version of the white paper was released in July 2016, which captured novel trends and key technological enablers for the realization of the 5G architecture vision along with harmonized architectural concepts from 5GPPP Phase 1 projects and initiatives. Capitalizing on the architectural vision and framework set by the first version of the white paper, this Version 2.0 of the white paper presents the latest findings and analyses with a particular focus on the concept evaluations, and accordingly it presents the consolidated overall architecture design

Data-Driven Network Management for Next-Generation Wireless Networks

With the commercialization and maturity of the fifth-generation (5G) wireless networks, the next-generation wireless network (NGWN) is envisioned to provide seamless connectivity for mobile user terminals (MUTs) and to support a wide range of new applications with stringent quality of service (QoS) requirements. In the NGWN, the network architecture will be highly heterogeneous due to the integration of terrestrial networks, satellite networks, and aerial networks formed by unmanned aerial vehicles (UAVs), and the network environment becomes highly dynamic because of the mobility of MUTs and the spatiotemporal variation of service demands. In order to provide high-quality services in such dynamic and heterogeneous networks, flexible, fine-grained, and adaptive network management will be essential. Recent advancements in deep learning (DL) and digital twins (DTs) have made it possible to enable data-driven solutions to support network management in the NGWN. DL methods can solve network management problems by leveraging data instead of explicit mathematical models, and DTs can facilitate DL methods by providing extensive data based on the full digital representations created for individual MUTs. Data-driven solutions that integrates DL and DT can address complicated network management problems and explore implicit network characteristics to adapt to dynamic network environments in the NGWN. However, the design of data-driven network management solutions in the NGWN meets several technical challenges: 1) how the NGWN can be configured to support multiple services with different spatiotemporal service demands while simultaneously satisfying their different QoS requirements; 2) how the multi-dimensional network resources are proactively reserved to support MUTs with different mobility patterns in a resource-efficient manner; and 3) how the heterogeneous NGWN components, including base stations (BSs), satellites, and UAVs, jointly coordinate their network resources to support dynamic service demands, etc. In this thesis, we develop efficient data-driven network management strategies in two stages, i.e., long-term network planning and real-time network operation, to address the above challenges in the NGWN. Firstly, we investigate planning-stage network configuration to satisfy different service requirements for communication services. We consider a two-tier network with one macro BS and multiple small BSs, which supports communication services with different spatiotemporal data traffic distributions. The objective is to maximize the energy efficiency of BSs by jointly configuring downlink transmission power and communication coverage for each BS. To achieve this objective, we first design a network planning scheme with flexible binary slice zooming, dual time-scale planning, and grid-based network planning. The scheme allows flexibility to differentiate the communication coverage and downlink transmission power of the same BS for different services while improving the temporal and spatial granularity of network planning. We formulate a combinatorial optimization problem in which communication coverage management and power control are mutually dependent. To solve the problem, we propose a data-driven method with two steps: 1) we propose an unsupervised-learning-assisted approach to determine the communication coverage of BSs; and 2) we derive a closed-form solution for power control. Secondly, we investigate planning-stage resource reservation for a compute-intensive service to support MUTs with different mobility patterns. The MUTs can offload their computing tasks to the computing servers deployed at the core networks, gateways, and BSs. Each computing server requires both computing and storage resources to execute computing tasks. The objective is to optimize long-term resource reservation by jointly minimizing the usage of computing, storage, and communication resources and the cost from re-configuring resource reservation. To this end, we develop a data-driven network planning scheme with two elements, i.e., multi-resource reservation and resource reservation re-configuration. First, DTs are designed for collecting MUT status data, based on which MUTs are grouped according to their mobility patterns. Then, an optimization algorithm is proposed to customize resource reservation for different groups to satisfy their different resource demands. Last, a meta-learning-based approach is proposed to re-configure resource reservation for balancing the network resource usage and the re-configuration cost. Thirdly, we investigate operation-stage computing resource allocation in a space-air-ground integrated network (SAGIN). A UAV is deployed to fly around MUTs and collect their computing tasks, while scheduling the collected computing tasks to be processed at the UAV locally or offloaded to the nearby BSs or the remote satellite. The energy budget of the UAV, intermittent connectivity between the UAV and BSs, and dynamic computing task arrival pose challenges in computing task scheduling. The objective is to design a real-time computing task scheduling policy for minimizing the delay of computing task offloading and processing in the SAGIN. To achieve the objective, we first formulate the on-line computing scheduling in the dynamic network environment as a constrained Markov decision process. Then, we develop a risk-sensitive reinforcement learning approach in which a risk value is used to represent energy consumption that exceeds the budget. By balancing the risk value and the reward from delay minimization, the UAV can explore the task scheduling policy to minimize task offloading and processing delay while satisfying the UAV energy constraint. Extensive simulation have been conducted to demonstrate that the proposed data-driven network management approach for the NGWN can achieve flexible BS configuration for multiple communication services, fine-grained multi-dimensional resource reservation for a compute-intensive service, and adaptive computing resource allocation in the dynamic SAGIN. The schemes developed in the thesis are valuable to the data-driven network planning and operation in the NGWN

New paradigms of distributed AI for improving 5G-based network systems performance

With the advent of 5G technology, there is an increasing need for efficient and effective machine learning techniques to support a wide range of applications, from smart cities to autonomous vehicles. The research question is whether distributed machine learning can provide a solution to the challenges of large-scale data processing, resource allocation, and privacy concerns in 5G networks. The thesis examines two main approaches to distributed machine learning: split learning and federated learning. Split learning enables the separation of model training and data storage between multiple devices, while federated learning allows for the training of a global model using decentralized data sources. The thesis investigates the performance of these approaches in terms of accuracy, communication overhead, and privacy preservation. The findings suggest that distributed machine learning can provide a viable solution to the challenges of 5G networks, with split learning and federated learning techniques showing promising results for spectral efficiency, resource allocation, and privacy preservation. The thesis concludes with a discussion of future research directions and potential applications of distributed machine learning in 5G networks. In this thesis, we investigate four case studies of both 5G network systems and LTE and Wifi (legacy parts). In chapter3, we implement an asynchronous federated learning model to predict the RSSI in robot localization indoor and outdoor environments. The proposed framework provides a good performance in terms of convergence, accuracy, and overhead reduction. In chapter4, we transfer the deployment of the asynchronous federated learning framework from the Wifi use case to a part of 5G networks (Network slicing), where we use the framework to predict the slice type for rapid and automated intelligent resource allocation. [...

Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System

With the rapid growth and development of cities, Intelligent Traffic Management and Control (ITMC) is becoming a fundamental component to address the challenges of modern urban traffic management, where a wide range of daily problems need to be addressed in a prompt and expedited manner. Issues such as unpredictable traffic dynamics, resource constraints, and abnormal events pose difficulties to city managers. ITMC aims to increase the efficiency of traffic management by minimizing the odds of traffic problems, by providing real-time traffic state forecasts to better schedule the intersection signal controls. Reliable implementations of ITMC improve the safety of inhabitants and the quality of life, leading to economic growth. In recent years, researchers have proposed different solutions to address specific problems concerning traffic management, ranging from image-processing and deep-learning techniques to forecasting the traffic state and deriving policies to control intersection signals. This review article studies the primary public datasets helpful in developing models to address the identified problems, complemented with a deep analysis of the works related to traffic state forecast and intersection-signal-control models. Our analysis found that deep-learning-based approaches for short-term traffic state forecast and multi-intersection signal control showed reasonable results, but lacked robustness for unusual scenarios, particularly during oversaturated situations, which can be resolved by explicitly addressing these cases, potentially leading to significant improvements of the systems overall. However, there is arguably a long path until these models can be used safely and effectively in real-world scenarios

Multi-access edge computing: A survey

Multi-access Edge Computing (MEC) is a key solution that enables operators to open their networks to new services and IT ecosystems to leverage edge-cloud benefits in their networks and systems. Located in close proximity from the end users and connected devices, MEC provides extremely low latency and high bandwidth while always enabling applications to leverage cloud capabilities as necessary. In this article, we illustrate the integration of MEC into a current mobile networks' architecture as well as the transition mechanisms to migrate into a standard 5G network architecture.We also discuss SDN, NFV, SFC and network slicing as MEC enablers. Then, we provide a state-of-the-art study on the different approaches that optimize the MEC resources and its QoS parameters. In this regard, we classify these approaches based on the optimized resources and QoS parameters (i.e., processing, storage, memory, bandwidth, energy and latency). Finally, we propose an architectural framework for a MEC-NFV environment based on the standard SDN architecture