ARENA: A Data-Driven Radio Access Networks Analysis of Football Events

Zanzi, Lanfranco; Sciancalepore, Vincenzo; García‐Saavedra, Andrés; Costa‐Pérez, Xavier; Agapiou, Georgios; Schotten, Hans D.

doi:10.1109/tnsm.2020.3032829

Cited by 5 publications

(3 citation statements)

References 34 publications

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“…To this aim, we generate traffic and mobility dataset for an increasing number of end-users, namely 15k, 20k, and 25k. As highlighted in [59], a non-linear relationship characterizes end-user mobility and throughput performances in crowded scenarios. Clearly, this also affects the communication latency, as a higher number of users will be simultaneously active under the same radio access node.…”

Section: ) Effects Of Different Network Loads and Mobilitymentioning

confidence: 99%

On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration

Rezazadeh¹,

Zanzi²,

Devoti³

et al. 2023

IEEE Trans. Veh. Technol.

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Network slicing enables multiple virtual networks to be instantiated and customized to meet heterogeneous use case requirements over 5G and beyond network deployments. However, most of the solutions available today face scalability issues when considering many slices, due to centralized controllers requiring a holistic view of the resource availability and consumption over different networking domains. In order to tackle this challenge, we design a hierarchical architecture to manage network slices resources in a federated manner. Driven by the rapid evolution of deep reinforcement learning (DRL) schemes and the Open RAN (O-RAN) paradigm, we propose a set of traffic-aware local decision agents (DAs) dynamically placed in the radio access network (RAN). These federated decision entities tailor their resource allocation policy according to the long-term dynamics of the underlying traffic, defining specialized clusters that enable faster training and communication overhead reduction. Indeed, aided by a traffic-aware agent selection algorithm, our proposed Federated DRL approach provides higher resource efficiency than benchmark solutions by quickly reacting to end-user mobility patterns and reducing costly interactions with centralized controllers.

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Section: ) Effects Of Different Network Loads and Mobilitymentioning

confidence: 99%

On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration

Rezazadeh¹,

Zanzi²,

Devoti³

et al. 2023

IEEE Trans. Veh. Technol.

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“…The resulting ROC and PR curves show better performances when compared against the baseline AE approach. This result can be explained through the ability of 3D-CNN network to capture spatio-temporal correlations between different network measurements [23]. However, when Recall value increases over a certain level (i.e., moving from left to right on the PR diagram), the Precision score drops drastically, suggesting poor performances when differentiating among different types of anomalies.…”

Section: E Performance Comparison and Practical Considerationsmentioning

confidence: 99%

$π$-ROAD: a Learn-as-You-Go Framework for On-Demand Emergency Slices in V2X Scenarios

Okic,

Zanzi,

Sciancalepore

et al. 2020

Preprint

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Vehicle-to-everything (V2X) is expected to become one of the main drivers of 5G business in the near future. Dedicated network slices are envisioned to satisfy the stringent requirements of advanced V2X services, such as autonomous driving, aimed at drastically reducing road casualties. However, as V2X services become more mission-critical, new solutions need to be devised to guarantee their successful service delivery even in exceptional situations, e.g. road accidents, congestion, etc. In this context, we propose π-ROAD, a deep learning framework to automatically learn regular mobile traffic patterns along roads, detect non-recurring events and classify them by severity level. π-ROAD enables operators to proactively instantiate dedicated Emergency Network Slices (ENS) as needed while re-dimensioning the existing slices according to their service criticality level. Our framework is validated by means of real mobile network traces collected within 400 km of a highway in Europe and augmented with publicly available information on related road events. Our results show that π-ROAD successfully detects and classifies nonrecurring road events and reduces up to 30% the impact of ENS on already running services.

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“…To this aim, we generate traffic and mobility dataset for an increasing number of end-users, namely 15k, 20k, and 25k. As highlighted in [103], a non-linear relationship characterizes end-user mobility and throughput performances in crowded scenarios. Clearly, this also affects the communication latency, as a higher number of users will be simultaneously active under the same radio access node.…”

Section: Effects Of Different Network Loads and Mobilitymentioning

confidence: 99%

Lifelong AI-driven zero-touch network slicing

Rezazadeh

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(English) The sixth-generation (6G) network's evolution necessitates advancements in algorithms and architecture to transition from an AI-native to an intrinsic trustworthy automation-native system. Network slicing creates multiple virtual networks for diverse service requirements and is a crucial technology for future communication systems and 6G networks. This technology will facilitate numerous aspects of life, society, and industry, meeting the communication needs of both humans and intelligent machines. This is made possible by employing softwarization and virtualization technologies, including software-defined networking (SDN) and network functions virtualization (NFV), to enable the necessary flexibility and programmability required for network slicing. However, the implementation of network slicing in radio access networks (RAN) is challenging due to the complexity of managing RAN operations. This gap is addressed with zero-touch network slicing, a fully automated management and orchestration scheme, which eliminates the need for fixed contracts and manual intervention. The decision engine, a main component of this technology, employs algorithmic innovation to optimize network resource allocation and handle the challenges of RAN slicing, including energy efficiency, latency, scalability, and trustworthiness. In resource allocation, a joint optimization approach is used to balance energy and latency with service quality. A stochastic Actor-Critic approach is proposed to streamline the learning procedure and reduce the need for hyperparameter tuning in an energy-aware network slicing setup. Additionally, a massive deep reinforcement learning-based actor-learner framework is introduced to tackle complexity issues and control challenges in network slicing. The temporal variations of traffic demand pose a significant challenge for resource planning and allocation in the RAN domain. To mitigate this, a distributed architecture for RAN slice resource orchestration is proposed, featuring multiple AI-enabled decision agents that perform local radio allocation decisions without a centralized control entity. A federated learning scheme, aligned with the recent development of the Open RAN architecture, is designed to enhance local decision-making capabilities. Despite AI's effectiveness, concerns persist regarding the lack of transparency in deep neural networks, posing risks to reliability and security in network scenarios. This lack of trust prevents telecommunications operators from widely deploying AI models in their networks. To address this, we proposed SliceOps architecture that consolidates explainable ML operations in a standalone slice, offering AI services to other slices. This setup enhances the reliability and interpretability of AI models, facilitating swift deployment in the network with greater consistency. By transitioning from AI-native to automation-native, this framework manages both service and underlying AI functions, allowing full potential harnessing of slicing in RAN while reducing complexities. (Español) La evolución hacia la red de sexta generación (6G) requiere avances en algoritmos y arquitectura para pasar de un sistema nativo de IA a un sistema nativo de automatización intrínsecamente confiable. El slicing de red crea múltiples redes virtuales para diversos requisitos de servicio y es una tecnología crucial para los sistemas de comunicación futuros y las redes 6G. Esta tecnología facilitará numerosos aspectos de la vida, la sociedad y la industria, satisfaciendo las necesidades de comunicación tanto de humanos como de máquinas inteligentes. Esto se hace posible mediante el uso de tecnologías de softwarización y virtualización, incluyendo redes definidas por software (SDN) y virtualización de funciones de red (NFV), que dotan la flexibilidad y la programabilidad necesarias para el slicing de red. Sin embargo, la implementación de slicing de red en las redes de acceso por radio (RAN) es todo un desafío debido a la complejidad de gestionar las operaciones en el dominio radio. Esta brecha se aborda con el slicing de red zero-touch, un esquema de gestión y orquestación totalmente automatizado, que elimina la necesidad de contratos fijos e intervención manual. El motor de decisiones, un componente principal de esta tecnología, emplea la innovación algorítmica para optimizar la asignación de recursos de la red y manejar los desafíos del slicing de RAN, incluyendo la eficiencia energética, la latencia, la escalabilidad y la confiabilidad. En la asignación de recursos, se utiliza un enfoque de optimización conjunta para equilibrar la energía y la latencia con la calidad del servicio. En concreto, se propone un enfoque Actor-Crítico estocástico para agilizar el procedimiento de aprendizaje y reducir la necesidad de ajustar hiperparámetros en una configuración de slicing de red consciente del consumo energético. Además, se introduce un marco de actor-aprendiz basado en un aprendizaje profundo masivo para abordar los problemas de complejidad y los desafíos de control en el slicing de red. A su vez, las variaciones temporales de la demanda de tráfico representan un desafío significativo para la planificación y asignación de recursos en el dominio de RAN. Para mitigar esto, se propone una arquitectura distribuida para la orquestación de recursos de slices de RAN, que cuenta con múltiples agentes de decisión habilitados por IA que materializan decisiones locales de asignación de radio sin una entidad de control centralizada. Se diseña un esquema de aprendizaje federado al respecto, alineado con el reciente desarrollo de la arquitectura Open RAN, para mejorar las capacidades de toma de decisiones locales. A pesar de la efectividad de la IA, persisten las preocupaciones sobre la falta de transparencia en las redes neuronales profundas, lo que supone riesgos para la fiabilidad y la seguridad en los escenarios de red. Esta falta de confianza impide que los operadores de telecomunicaciones desplieguen ampliamente modelos de IA en sus redes. Para abordar esto, propusimos la arquitectura SliceOps que consolida las operaciones de ML explicables en un slice independiente, ofreciendo servicios de IA a otros slices. Esta configuración mejora la fiabilidad y la interpretabilidad de los modelos de IA, facilitando una implementación rápida en la red con mayor consistencia. Al pasar de ser nativo de IA a nativo de automatización, este marco gestiona tanto las funciones de servicio como las subyacentes de IA, permitiendo el aprovechamiento total del slicing en RAN y reduciendo las complejidades.

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ARENA: A Data-Driven Radio Access Networks Analysis of Football Events

Cited by 5 publications

References 34 publications

On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration

On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration

$π$-ROAD: a Learn-as-You-Go Framework for On-Demand Emergency Slices in V2X Scenarios

Lifelong AI-driven zero-touch network slicing

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