Tesi etd-09132024-175037
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Tipo di tesi
Dottorato
Autore
PAOLINI, EMILIO
URN
etd-09132024-175037
Titolo
AI in NextG Networks: from Neuromorphic Hardware to Applications
Settore scientifico disciplinare
ING-INF/03
Corso di studi
Istituto di Tecnologie della Comunicazione, dell'Informazione e della Percezione - PHD IN EMERGING DIGITAL TECHNOLOGIES
Commissione
relatore VALCARENGHI, LUCA
Presidente Prof. ESPOSITO, FLAVIO
Membro Prof.ssa BOGONI, ANTONELLA
Membro Dott. PEDRO, JOAO
Presidente Prof. ESPOSITO, FLAVIO
Membro Prof.ssa BOGONI, ANTONELLA
Membro Dott. PEDRO, JOAO
Parole chiave
- Photonic AI
- Wireless Networks
- Network Intelligence
Data inizio appello
05/11/2024;
Disponibilità
completa
Riassunto analitico
The increasing complexity and demand for ultra-low latency, high data rates, and massive
device connectivity in Next Generation (NextG) wireless networks require new paradigms
for network management. As wireless technologies evolve, the integration of Artificial
Intelligence (AI) becomes essential to effectively address the dynamic nature of NextG
systems. AI, particularly Deep Learning (DL), offers the potential to automate resource
management, optimize performance in real time, and enable emerging applications, such
as immersive augmented reality, holographic telepresence, and unmanned mobility.
This thesis introduces a comprehensive multi-layered approach to integrating AI tech-
nologies into NextG networks, spanning from neuromorphic photonic hardware to middle-
ware and system-wide implementation. At the hardware level, we introduce Photonic-Aware
Neural Networks (PANNs), a novel class of neural networks that leverage the parallelism,
speed, and energy efficiency of neuromorphic photonic accelerators. PANNs demonstrate
significant improvements over traditional electronic systems in high-throughput, low-power
processing, essential for AI-driven tasks in NextG networks. We demonstrate their applica-
tions in computer vision and network security tasks, tackle the noise challenges introduced
by photonic accelerators, and explore hardware implementations.
Moving to the middleware level, we discuss the integration of In-Network Machine
Learning (ML) techniques that allow real-time decision-making directly within the network
infrastructure. By deploying ML models in network devices such as switches and routers, the
network can perform tasks like traffic management and cybersecurity threat detection with
reduced latency and computational load. This approach enhances the overall intelligence
and responsiveness of the network by processing data directly within the network devices.
Finally, at the system-wide orchestration level, we propose a converged Radio Access
Network-Core Network (RAN-CN) architecture, enabling real-time AI-driven analytics
directly at the base station. In addition, we introduce AI-powered resource management
techniques that dynamically scale network resources based on traffic forecasting, ensuring
efficient pro-active resource utilization. Furthermore, we investigate Federated Learning
(FL) techniques for distributed AI model training at the edge of the network, supported
by Fountain Codes (FC) to ensure reliable model updates even under challenging network
conditions.
Through these layers of integration, this thesis demonstrates how AI solutions can
enhance the scalability, efficiency, and sustainability of NextG wireless networks, making
them more adaptive and capable of addressing the demands of future wireless applications.
device connectivity in Next Generation (NextG) wireless networks require new paradigms
for network management. As wireless technologies evolve, the integration of Artificial
Intelligence (AI) becomes essential to effectively address the dynamic nature of NextG
systems. AI, particularly Deep Learning (DL), offers the potential to automate resource
management, optimize performance in real time, and enable emerging applications, such
as immersive augmented reality, holographic telepresence, and unmanned mobility.
This thesis introduces a comprehensive multi-layered approach to integrating AI tech-
nologies into NextG networks, spanning from neuromorphic photonic hardware to middle-
ware and system-wide implementation. At the hardware level, we introduce Photonic-Aware
Neural Networks (PANNs), a novel class of neural networks that leverage the parallelism,
speed, and energy efficiency of neuromorphic photonic accelerators. PANNs demonstrate
significant improvements over traditional electronic systems in high-throughput, low-power
processing, essential for AI-driven tasks in NextG networks. We demonstrate their applica-
tions in computer vision and network security tasks, tackle the noise challenges introduced
by photonic accelerators, and explore hardware implementations.
Moving to the middleware level, we discuss the integration of In-Network Machine
Learning (ML) techniques that allow real-time decision-making directly within the network
infrastructure. By deploying ML models in network devices such as switches and routers, the
network can perform tasks like traffic management and cybersecurity threat detection with
reduced latency and computational load. This approach enhances the overall intelligence
and responsiveness of the network by processing data directly within the network devices.
Finally, at the system-wide orchestration level, we propose a converged Radio Access
Network-Core Network (RAN-CN) architecture, enabling real-time AI-driven analytics
directly at the base station. In addition, we introduce AI-powered resource management
techniques that dynamically scale network resources based on traffic forecasting, ensuring
efficient pro-active resource utilization. Furthermore, we investigate Federated Learning
(FL) techniques for distributed AI model training at the edge of the network, supported
by Fountain Codes (FC) to ensure reliable model updates even under challenging network
conditions.
Through these layers of integration, this thesis demonstrates how AI solutions can
enhance the scalability, efficiency, and sustainability of NextG wireless networks, making
them more adaptive and capable of addressing the demands of future wireless applications.
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