Adaptive Digital Twin and Communication-Efficient Federated Learning Network Slicing for 5G-enabled Internet of Things

• URL: http://arxiv.org/abs/2407.10987v1
• Date: Sat, 22 Jun 2024 15:33:35 GMT
• Title: Adaptive Digital Twin and Communication-Efficient Federated Learning Network Slicing for 5G-enabled Internet of Things
• Authors: Daniel Ayepah-Mensah, Guolin Sun, Yu Pang, Wei Jiang,
• Abstract summary: Network slicing enables industrial Internet of Things (IIoT) networks with multiservice and differentiated resource requirements to meet increasing demands through efficient use and management of network resources. The new generation of Industry 4.0 has introduced digital twins to map physical systems to digital models for accurate decision-making. In our approach, we first use graph-attention networks to build a digital twin environment for network slices, enabling real-time traffic analysis, monitoring, and demand forecasting.
• Score: 8.11509914300497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Network slicing enables industrial Internet of Things (IIoT) networks with multiservice and differentiated resource requirements to meet increasing demands through efficient use and management of network resources. Typically, the network slice orchestrator relies on demand forecasts for each slice to make informed decisions and maximize resource utilization. The new generation of Industry 4.0 has introduced digital twins to map physical systems to digital models for accurate decision-making. In our approach, we first use graph-attention networks to build a digital twin environment for network slices, enabling real-time traffic analysis, monitoring, and demand forecasting. Based on these predictions, we formulate the resource allocation problem as a federated multi-agent reinforcement learning problem and employ a deep deterministic policy gradient to determine the resource allocation policy while preserving the privacy of the slices. Our results demonstrate that the proposed approaches can improve the accuracy of demand prediction for network slices and reduce the communication overhead of dynamic network slicing.

Related papers

• Cellular Traffic Prediction Using Online Prediction Algorithms [5.416701003120508]
  This paper investigates the efficacy of live prediction algorithms for forecasting cellular network traffic in real-time scenarios. We apply two live prediction algorithms on machine learning models, one of which is recently proposed Fast LiveStream Prediction (FLSP) algorithm. Our study reveals that the FLSP algorithm can halve the required bandwidth for asynchronous data reporting compared to conventional online prediction algorithms.
  arXiv  Detail & Related papers  (2024-05-08T17:36:14Z)
• Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks [94.2860766709971]
  We address the challenge of sampling and remote estimation for autoregressive Markovian processes in a wireless network with statistically-identical agents. Our goal is to minimize time-average estimation error and/or age of information with decentralized scalable sampling and transmission policies.
  arXiv  Detail & Related papers  (2024-04-04T06:24:11Z)
• Digital Twin-Enhanced Deep Reinforcement Learning for Resource Management in Networks Slicing [46.65030115953947]
  We propose a framework consisting of a digital twin and reinforcement learning agents. Specifically, we propose to use the historical data and the neural networks to build a digital twin model to simulate the state variation law of the real environment. We also extend the framework to offline reinforcement learning, where solutions can be used to obtain intelligent decisions based solely on historical data.
  arXiv  Detail & Related papers  (2023-11-28T15:25:14Z)
• Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks [0.0]
  The complexity and diversity of Telecom networks place an increasing strain on maintenance and operation efforts. Strict security and privacy requirements present a challenge for mobile operators to leverage network data. We propose a Bi-level Federated Graph Neural Network anomaly detection and diagnosis model.
  arXiv  Detail & Related papers  (2023-11-24T13:23:54Z)
• Federated Learning over Wireless IoT Networks with Optimized Communication and Resources [98.18365881575805]
  Federated learning (FL) as a paradigm of collaborative learning techniques has obtained increasing research attention. It is of interest to investigate fast responding and accurate FL schemes over wireless systems. We show that the proposed communication-efficient federated learning framework converges at a strong linear rate.
  arXiv  Detail & Related papers  (2021-10-22T13:25:57Z)
• Multi-Exit Semantic Segmentation Networks [78.44441236864057]
  We propose a framework for converting state-of-the-art segmentation models to MESS networks. specially trained CNNs that employ parametrised early exits along their depth to save during inference on easier samples. We co-optimise the number, placement and architecture of the attached segmentation heads, along with the exit policy, to adapt to the device capabilities and application-specific requirements.
  arXiv  Detail & Related papers  (2021-06-07T11:37:03Z)
• Deep Learning-based Resource Allocation For Device-to-Device Communication [66.74874646973593]
  We propose a framework for the optimization of the resource allocation in multi-channel cellular systems with device-to-device (D2D) communication. A deep learning (DL) framework is proposed, where the optimal resource allocation strategy for arbitrary channel conditions is approximated by deep neural network (DNN) models. Our simulation results confirm that near-optimal performance can be attained with low time, which underlines the real-time capability of the proposed scheme.
  arXiv  Detail & Related papers  (2020-11-25T14:19:23Z)
• Reinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicing [3.509171590450989]
  The paper presents a reinforcement learning solution to dynamic resource allocation for 5G radio network slicing. Results show that reinforcement learning provides major improvements in the 5G network utility relative to myopic, random, and first come first served solutions.
  arXiv  Detail & Related papers  (2020-09-14T17:10:17Z)
• Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks [1.0934800950965335]
  We develop a nonautore graph-based neural network for multistep network traffic forecasting. We evaluate the efficacy of our approach on real traffic from ESnet, the U.S. Department of Energy's dedicated science network.
  arXiv  Detail & Related papers  (2020-08-28T17:47:11Z)
• Wireless Power Control via Counterfactual Optimization of Graph Neural Networks [124.89036526192268]
  We consider the problem of downlink power control in wireless networks, consisting of multiple transmitter-receiver pairs communicating over a single shared wireless medium. To mitigate the interference among concurrent transmissions, we leverage the network topology to create a graph neural network architecture. We then use an unsupervised primal-dual counterfactual optimization approach to learn optimal power allocation decisions.
  arXiv  Detail & Related papers  (2020-02-17T07:54:39Z)

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.