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Found 14 papers, 3 papers with code
Machine Learning for Pre/Post Flight UAV Rotor Defect Detection Using Vibration Analysis
Unmanned Aerial Vehicles (UAVs) will be critical infrastructural components of future smart cities.
Semantic Routing for Enhanced Performance of LLM-Assisted Intent-Based 5G Core Network Management and Orchestration
Large language models (LLMs) are rapidly emerging in Artificial Intelligence (AI) applications, especially in the fields of natural language processing and generative AI.
Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networks
The integration of Machine Learning and Artificial Intelligence (ML/AI) into fifth-generation (5G) networks has made evident the limitations of network intelligence with ever-increasing, strenuous requirements for current and next-generation devices.
Leveraging Large Language Models for DRL-Based Anti-Jamming Strategies in Zero Touch Networks
As the dawn of sixth-generation (6G) networking approaches, it promises unprecedented advancements in communication and automation.
An NWDAF Approach to 5G Core Network Signaling Traffic: Analysis and Characterization
Data-driven approaches and paradigms have become promising solutions to efficient network performances through optimization.
A Model Drift Detection and Adaptation Framework for 5G Core Networks
Due to the dynamic nature of next-generation networks and their supported use cases and applications, model drift is a serious concern, which can deteriorate the performance of intelligent models deployed throughout the network.
Towards Supporting Intelligence in 5G/6G Core Networks: NWDAF Implementation and Initial Analysis
Wireless networks, in the fifth-generation and beyond, must support diverse network applications which will support the numerous and demanding connections of today's and tomorrow's devices.
Concept Drift Detection in Federated Networked Systems
As next-generation networks materialize, increasing levels of intelligence are required.
PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams
As the number of Internet of Things (IoT) devices and systems have surged, IoT data analytics techniques have been developed to detect malicious cyber-attacks and secure IoT systems; however, concept drift issues often occur in IoT data analytics, as IoT data is often dynamic data streams that change over time, causing model degradation and attack detection failure.
Making a Case for Federated Learning in the Internet of Vehicles and Intelligent Transportation Systems
With the incoming introduction of 5G networks and the advancement in technologies, such as Network Function Virtualization and Software Defined Networking, new and emerging networking technologies and use cases are taking shape.
The Need for Advanced Intelligence in NFV Management and Orchestration
Through the adoption of Advanced Intelligence techniques such as Reinforcement Learning and Federated Learning, NSPs can leverage the benefits of traditional ML while simultaneously addressing the major challenges traditionally associated with it.
A Machine Learning-Based Migration Strategy for Virtual Network Function Instances
With the growing demand for data connectivity, network service providers are faced with the task of reducing their capital and operational expenses while simultaneously improving network performance and addressing the increased demand.
Depth-Optimized Delay-Aware Tree (DO-DAT) for Virtual Network Function Placement
With the constant increase in demand for data connectivity, network service providers are faced with the task of reducing their capital and operational expenses while ensuring continual improvements to network performance.
Machine Learning for Performance-Aware Virtual Network Function Placement
The model takes several performance-related features from the network as an input and selects the placement of the various VNF instances on network servers with the objective of minimizing the delay between dependent VNF instances.
