1,720,967 research outputs found
Analisi e progettazione di modelli, algoritmi e architetture per reti cellulari di prossima generazione
Full text linkA causa del sempre maggior numero di utenti, della crescente domanda di dati mobili e della nascita di nuove applicazioni, le reti cellulari necessitano di costante aggiornamento. L’ultima generazione di reti mobili, le reti 5G, è caratterizzata da elevate prestazioni ed estrema flessibilità, grazie alle quali è possibile supportare vari casi d’uso con diversi requisiti di servizio. Le comunicazioni a frequenze millimetriche rappresentano una delle principali novità dello standard 5G, perché consentono l’utilizzo di una vasta porzione di risorse radio ed il raggiungimento di elevate velocità di trasmissione. Tuttavia, la realizzazione di sistemi cellulari operanti a tali frequenze è soggetta a numerose problematiche che derivano dalle severe condizioni di propagazione dei segnali radio.
Questa tesi si pone l’obiettivo di fornire soluzioni innovative per risolvere le problematiche realizzative e sfruttare appieno i benefici di questa tecnologia. Nello specifico, (i) vengono presentati nuovi strumenti per la simulazione delle reti di prossima generazione, tra cui un modello di canale e modelli per la caratterizzazione delle antenne e delle operazioni di beamforming; (ii) vengono identificati i benefici e le problematiche relative alla realizzazione di reti millimetriche con backhaul senza fili e viene presentato un efficiente meccanismo di ripartizione delle risorse; (iii) viene analizzata l’interazione cross-layer che deriva dall’utilizzo congiunto di soluzioni HBF e MU-MIMO; (iv) viene introdotto un sistema innovativo per l’implementazione del paradigma di network slicing all’interno di una rete di accesso a frequenze millimetriche e, infine, (v) viene valutata la possibilità di supportare servizi di comunicazioni veicolare attraverso comunicazioni a frequenze millimetriche. L’approccio di tipo “system-level” utilizzato in questa tesi permette di caratterizzare il comportamento della rete in modo adeguato, prendendo in considerazione l’intero stack protocollare e tutti gli elementi che influenzano le prestazioni degli utenti finali. I risultati ottenuti dimostrano l’efficacia delle soluzioni proposte, aprendo nuove strade per la realizzazione di reti cellulari più efficienti e performanti.The always-increasing number of mobile subscribers, the growing demand for mobile data, and the emergence of new applications require cellular systems to be constantly improved. The last generation of cellular networks, i.e., 5G, stands out for its high performance and extreme flexibility, making it possible to support multiple use cases with diverse and stringent service requirements. One of the main novelties is represented by the possibility to communicate at millimeter wave (mmWave) frequencies, providing access to an unprecedented amount of radio resources which can theoretically enable extremely high data rates. However, signals propagating at these frequencies experience harsh conditions, posing several challenges for the realization of efficient mmWave cellular systems.
The grand objective of this thesis is to provide innovative solutions to overcome the limitations of mmWave communications and exploit the potential of this technology in the context of 5G and beyond cellular networks. In particular, we (i) present novel simulation tools, including channel, antenna, and beamforming models for the accurate characterization of next-generation cellular systems; (ii) identify the potential and challenges for the realization of wireless-backhauled mmWave deployments, and present a semi-centralized resource partitioning scheme for this type of networks; (iii) analyze the cross-layer challenges arising from the integration of Hybrid Beamforming (HBF) and Multi User MIMO (MU-MIMO) in mmWave cellular systems; (iv) introduce a novel framework to enable network slicing in mmWave Radio Access Networks (RANs); and (v) evaluate the feasibility of providing vehicular communication services by means of mmWave communications. We adopt a system-level approach that allow us to properly characterize the network behavior, considering the full protocol stack and all the elements that have an impact on the performance of the end-users. Our results demonstrate the effectiveness of the proposed solutions, breaking new ground towards more efficient and high-performance mmWave cellular systems
Enabling RAN Slicing Through Carrier Aggregation in mmWave Cellular Networks
No full textThe ever increasing number of connected devices and of new and heterogeneous mobile use cases implies that 5G cellular systems will face demanding technical challenges. For example, Ultra-Reliable Low-Latency Communication (URLLC) and enhanced Mobile Broadband (eMBB) scenarios present orthogonal Quality of Service (QoS) requirements that 5G aims to satisfy with a unified Radio Access Network (RAN) design. Network slicing and mmWave communications have been identified as possible enablers for 5G. They provide, respectively, the necessary scalability and flexibility to adapt the network to each specific use case environment, and low latency and multi-gigabit-per-second wireless links, which tap into a vast, currently unused portion of the spectrum. The optimization and integration of these technologies is still an open research challenge, which requires innovations at different layers of the protocol stack. This paper proposes to combine them in a RAN slicing framework for mmWaves, based on carrier aggregation. Notably, we introduce MilliSlice, a cross-carrier scheduling policy that exploits the diversity of the carriers and maximizes their utilization, thus simultaneously guaranteeing high throughput for the eMBB slices and low latency and high reliability for the URLLC flows
Integrated Access and Backhaul in 5G mmWave Networks: Potential and Challenges
No full textIAB is being considered as a means to reduce the deployment costs of ultra-dense 5G mmWave networks, using wireless backhaul links to relay the access traffic. In this work we describe the most recent standardization activities on IAB, and compare architectures with and without IAB in mmWave deployments. While it is well understood that IAB networks reduce deployment costs by obviating the need to provide wired backhaul to each cellular base station, it is still necessary to validate the IAB performance in realistic scenarios. In this article we demonstrate the cell edge throughput advantage offered by IAB using endto- end system-level simulations. We also highlight some research challenges for IAB that will require further investigations
PRATA: A Framework to Enable Predictive QoS in Vehicular Networks via Artificial Intelligence
No full textPredictive Quality of Service (PQoS) makes it possible to anticipate QoS changes, e.g., in wireless networks, and trigger appropriate countermeasures to avoid performance degradation. A promising tool for PQoS is given by Reinforcement Learning (RL), a methodology that enables the design of decision-making strategies for stochastic optimization. In this manuscript, we present PRATA, a new simulation framework to enable PRedictive QoS based on AI for Teleoperated driving Applications. PRATA consists of a modular pipeline that includes (i) an end-to-end protocol stack to simulate the 5G Radio Access Network (RAN), (ii) a tool for generating automotive data, and (iii) an Artificial Intelligence (AI) unit to optimize PQoS decisions. To prove its utility, we use PRATA to design an RL unit, named RAN-AI, to optimize the segmentation level of teleoperated driving data in the event of resource saturation or channel degradation. Hence, we show that the RAN-AI entity efficiently balances the trade-off between QoS and Quality of Experience (QoE) that characterize teleoperated driving applications, almost doubling the system performance compared to baseline approaches. In addition, by varying the learning settings of the RAN-AI entity, we investigate the impact of the state space and the relative cost of acquiring network data that are necessary for the implementation of RL
Towards AI-native vehicular communications
No full textThe role of fast yet reliable wireless communications in various application domains is getting ever more important. At the same time, as use cases are becoming more and more complex, application requirements are getting ever more stringent. One example is intelligent transportation, where the efficiency and reliability of wireless data delivery is essential for effective service support. As a consequence, in this context the adoption of AI techniques is widely considered crucial for enabling vehicular communications to adapt to dynamic changes of the environment. In this position paper, we discuss some representative applications of advanced AI tools in vehicular communications. In particular, we elaborate on the potential of distributed learning based on federated learning, of proactive service provisioning, and of graph neural network for enabling AI-native vehicular communications
ns-O-RAN: Simulating O-RAN 5G Systems in ns-3
Full text linkO-RAN is radically shifting how cellular networks are designed, deployed and
optimized through network programmability, disaggregation, and virtualization.
Specifically, RAN Intelligent Controllers (RICs) can orchestrate and optimize
the Radio Access Network (RAN) operations, allowing fine-grained control over
the network. RICs provide new approaches and solutions for classical use cases
such as on-demand traffic steering, anomaly detection, and Quality of Service
(QoS) management, with an optimization that can target single User Equipments
(UEs), slices, cells, or entire base stations. While this comes with the
potential to enable intelligent, programmable RANs, there are still significant
challenges to be faced, primarily related to data collection at scale,
development and testing of custom control logic for the RICs, and availability
of Open RAN simulation and experimental tools for the research and development
communities. To address this, we introduce ns-O-RAN, a software integration
between a real-world near-real-time RIC and an ns-3 simulated RAN which
provides a platform for researchers and telco operators to build, test and
integrate xApps. ns-O-RAN extends a popular Open RAN experimental framework
(OpenRAN Gym) with simulation capabilities that enable the generation of
realistic datasets without the need for experimental infrastructure. We
implement it as a new open-source ns-3 module that uses the E2 interface to
connect different simulated 5G base stations with the RIC, enabling the
exchange of E2 messages and RAN KPMs to be consumed by standard xApps.
Furthermore, we test ns-O-RAN with the OSC and OpenRAN Gym RICs, simplifying
the onboarding from a test environment to production with real telecom hardware
controlled without major reconfigurations required. ns-O-RAN is open source and
publicly available, together with quick-start tutorials and documentation.Comment: 10 pages, 6 figures, 1 table, 5 listings. Accepted at the 2023
Workshop on ns-3 (WNS3 2023), June 28-29, 2023, Arlington, VA, USA. ACM, New
York, NY, US
Implementation of A Spatial Channel Model for ns-3
No full textThe next generation of wireless networks will feature a more flexible radio access design, integrating multiple new technological solutions (e.g., massive Multiple-Input Multiple-Output (MIMO), millimeter waves) to satisfy different verticals and use cases. The performance evaluation of these networks will require more complex models to represent the interactions of different components of the networks accurately. For example, channel models, which are of paramount importance to precisely characterize the behavior of such systems, need to account for multi-antenna systems and new frequency bands. This paper presents the ns-3 implementation of a spatial channel model for the 0.5-100 GHz spectrum, following the 3GPP Technical Report 38.901. The code, designed to be flexible and easily extensible, is integrated in ns-3's antenna, propagation and spectrum models, and offers the support for the investigation of future wireless systems in ns-3
Full-stack Hybrid Beamforming in mmWave 5G Networks
No full textThis paper analyzes Hybrid Beamforming (HBF) and Multi-User Multiple-Input Multiple-Output (MU-MIMO) in millimeter wave (mmWave) 5th generation (5G) cellular networks considering the full protocol stack with TCP/IP traffic and MAC scheduling. Prior work on HBF and MU-MIMO has assumed full-buffer transmissions and studied link-level performance. We report non-trivial interactions between the HBF technique, the front-loaded channel estimation pilot scheme in NR, and the constraints of MU-MIMO scheduling. We also report that joint multi-user beamforming design is imperative, in the sense that the MU-MIMO system cannot be fully exploited when implemented as a mere collection of single-user analog beams working in parallel. By addressing these issues, throughput can be dramatically increased in mmWave 5G networks by means of Spatial Division Multiple Access (SDMA)
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