Synergizing AI and Digital Twins for Next-Generation Network Optimization, Forecasting, and Security

• URL: http://arxiv.org/abs/2503.06302v1
• Date: Sat, 08 Mar 2025 18:30:54 GMT
• Title: Synergizing AI and Digital Twins for Next-Generation Network Optimization, Forecasting, and Security
• Authors: Zifan Zhang, Minghong Fang, Dianwei Chen, Xianfeng Yang, Yuchen Liu,
• Abstract summary: Digital network twins (DNTs) are virtual representations of physical networks, designed to enable real-time monitoring, simulation, and optimization of network performance.<n>When integrated with machine learning (ML) techniques, DNTs emerge as powerful solutions for managing the complexities of network operations.<n>We highlight key technical challenges that need to be addressed, such as ensuring network reliability, achieving joint data-scenario forecasting, and maintaining security in high-risk environments.
• Score: 4.6313441815490775
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Digital network twins (DNTs) are virtual representations of physical networks, designed to enable real-time monitoring, simulation, and optimization of network performance. When integrated with machine learning (ML) techniques, particularly federated learning (FL) and reinforcement learning (RL), DNTs emerge as powerful solutions for managing the complexities of network operations. This article presents a comprehensive analysis of the synergy of DNTs, FL, and RL techniques, showcasing their collective potential to address critical challenges in 6G networks. We highlight key technical challenges that need to be addressed, such as ensuring network reliability, achieving joint data-scenario forecasting, and maintaining security in high-risk environments. Additionally, we propose several pipelines that integrate DNT and ML within coherent frameworks to enhance network optimization and security. Case studies demonstrate the practical applications of our proposed pipelines in edge caching and vehicular networks. In edge caching, the pipeline achieves over 80% cache hit rates while balancing base station loads. In autonomous vehicular system, it ensure a 100% no-collision rate, showcasing its reliability in safety-critical scenarios. By exploring these synergies, we offer insights into the future of intelligent and adaptive network systems that automate decision-making and problem-solving.

Related papers

• Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks [39.08784216413478]
  This paper proposes an automated security framework targeting Physical Layer Authentication and Cross-Layer Intrusion Detection Systems.<n>The proposed framework employs drift-adaptive online learning techniques and a novel enhanced Successive Halving (SH)-based Automated ML (AutoML) method to automatically generate optimized ML models for dynamic networking environments.
  arXiv  Detail & Related papers  (2025-02-28T01:16:11Z)
• Achieving Network Resilience through Graph Neural Network-enabled Deep Reinforcement Learning [64.20847540439318]
  Deep reinforcement learning (DRL) has been widely used in many important tasks of communication networks.<n>Some studies have combined graph neural networks (GNNs) with DRL, which use the GNNs to extract unstructured features of the network.<n>This paper explores the solution of combining GNNs with DRL to build a resilient network.
  arXiv  Detail & Related papers  (2025-01-19T15:22:17Z)
• SafePowerGraph: Safety-aware Evaluation of Graph Neural Networks for Transmission Power Grids [55.35059657148395]
  We present SafePowerGraph, the first simulator-agnostic, safety-oriented framework and benchmark for Graph Neural Networks (GNNs) in power systems (PS) operations. SafePowerGraph integrates multiple PF and OPF simulators and assesses GNN performance under diverse scenarios, including energy price variations and power line outages.
  arXiv  Detail & Related papers  (2024-07-17T09:01:38Z)
• Securing Distributed Network Digital Twin Systems Against Model Poisoning Attacks [19.697853431302768]
  Digital twins (DTs) embody real-time monitoring, predictive, and enhanced decision-making capabilities.<n>This study investigates the security challenges in distributed network DT systems, which potentially undermine the reliability of subsequent network applications.
  arXiv  Detail & Related papers  (2024-07-02T03:32:09Z)
• Integrating Generative AI with Network Digital Twins for Enhanced Network Operations [0.0]
  This paper explores the synergy between network digital twins and generative AI. We show how generative AI can enhance the accuracy and operational efficiency of network digital twins.
  arXiv  Detail & Related papers  (2024-06-24T19:54:58Z)
• DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach [49.56404236394601]
  We formulate the problem of joint DNN partitioning, task offloading, and resource allocation in Vehicular Edge Computing. Our objective is to minimize the DNN-based task completion time while guaranteeing the system stability over time. We propose a Multi-Agent Diffusion-based Deep Reinforcement Learning (MAD2RL) algorithm, incorporating the innovative use of diffusion models.
  arXiv  Detail & Related papers  (2024-06-11T06:31:03Z)
• ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and Multispectral Data Fusion [54.668445421149364]
  Deep learning-based hyperspectral image (HSI) super-resolution aims to generate high spatial resolution HSI (HR-HSI) by fusing hyperspectral image (HSI) and multispectral image (MSI) with deep neural networks (DNNs) In this letter, we propose a novel adversarial automatic data augmentation framework ADASR that automatically optimize and augments HSI-MSI sample pairs to enrich data diversity for HSI-MSI fusion.
  arXiv  Detail & Related papers  (2023-10-11T07:30:37Z)
• Teal: Learning-Accelerated Optimization of WAN Traffic Engineering [68.7863363109948]
  We present Teal, a learning-based TE algorithm that leverages the parallel processing power of GPUs to accelerate TE control. To reduce the problem scale and make learning tractable, Teal employs a multi-agent reinforcement learning (RL) algorithm to independently allocate each traffic demand. Compared with other TE acceleration schemes, Teal satisfies 6--32% more traffic demand and yields 197--625x speedups.
  arXiv  Detail & Related papers  (2022-10-25T04:46:30Z)
• AI in 6G: Energy-Efficient Distributed Machine Learning for Multilayer Heterogeneous Networks [7.318997639507269]
  We propose a novel layer-based HetNet architecture which distributes tasks associated with different machine learning approaches across network layers and entities. Such a HetNet boasts multiple access schemes as well as device-to-device (D2D) communications to enhance energy efficiency.
  arXiv  Detail & Related papers  (2022-06-04T22:03:19Z)
• Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework [13.573645522781712]
  Deep neural networks (DNNs) and spiking neural networks (SNNs) offer state-of-the-art results on resource-constrained edge devices. These systems are required to maintain correct functionality under diverse security and reliability threats. This paper first discusses existing approaches to address energy efficiency, reliability, and security issues at different system layers.
  arXiv  Detail & Related papers  (2021-09-20T20:22:56Z)
• Cognitive Radio Network Throughput Maximization with Deep Reinforcement Learning [58.44609538048923]
  Radio Frequency powered Cognitive Radio Networks (RF-CRN) are likely to be the eyes and ears of upcoming modern networks such as Internet of Things (IoT) To be considered autonomous, the RF-powered network entities need to make decisions locally to maximize the network throughput under the uncertainty of any network environment. In this paper, deep reinforcement learning is proposed to overcome the shortcomings and allow a wireless gateway to derive an optimal policy to maximize network throughput.
  arXiv  Detail & Related papers  (2020-07-07T01:49:07Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.