Digital Twin-Based Multiple Access Optimization and Monitoring via Model-Driven Bayesian Learning

• URL: http://arxiv.org/abs/2210.05582v1
• Date: Tue, 11 Oct 2022 16:14:43 GMT
• Title: Digital Twin-Based Multiple Access Optimization and Monitoring via Model-Driven Bayesian Learning
• Authors: Clement Ruah, Osvaldo Simeone, Bashir Al-Hashimi
• Abstract summary: Digital twin (DT) platforms are seen as a promising paradigm to control and monitor software-based, "open", communication systems. In this work, the DT builds a Bayesian model of the communication system, which is leveraged to enable core DT functionalities. We specifically investigate the application of the proposed framework to a simple case-study system encompassing multiple sensing devices.
• Score: 30.62844128870099
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Commonly adopted in the manufacturing and aerospace sectors, digital twin (DT) platforms are increasingly seen as a promising paradigm to control and monitor software-based, "open", communication systems, which play the role of the physical twin (PT). In the general framework presented in this work, the DT builds a Bayesian model of the communication system, which is leveraged to enable core DT functionalities such as control via multi-agent reinforcement learning (MARL) and monitoring of the PT for anomaly detection. We specifically investigate the application of the proposed framework to a simple case-study system encompassing multiple sensing devices that report to a common receiver. The Bayesian model trained at the DT has the key advantage of capturing epistemic uncertainty regarding the communication system, e.g., regarding current traffic conditions, which arise from limited PT-to-DT data transfer. Experimental results validate the effectiveness of the proposed Bayesian framework as compared to standard frequentist model-based solutions.

Related papers

• AAD-LLM: Adaptive Anomaly Detection Using Large Language Models [35.286105732902065]
  The research aims to improve the transferability of anomaly detection models by leveraging Large Language Models (LLMs) The research also seeks to enable more collaborative decision-making between the model and plant operators.
  arXiv  Detail & Related papers  (2024-11-01T13:43:28Z)
• Semantic Communication for Cooperative Perception using HARQ [51.148203799109304]
  We leverage an importance map to distill critical semantic information, introducing a cooperative perception semantic communication framework. To counter the challenges posed by time-varying multipath fading, our approach incorporates the use of frequency-division multiplexing (OFDM) along with channel estimation and equalization strategies. We introduce a novel semantic error detection method that is integrated with our semantic communication framework in the spirit of hybrid automatic repeated request (HARQ)
  arXiv  Detail & Related papers  (2024-08-29T08:53:26Z)
• DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection [52.74152717667157]
  We propose a lightweight module called Dual Attention Module (DAM) for capturing cross-dimension interaction relationships in-temporal skeletal data. It employs the frame attention mechanism to identify the most significant frames and the skeleton attention mechanism to capture broader relationships across fixed partitions with minimal parameters and flops.
  arXiv  Detail & Related papers  (2024-06-05T06:18:03Z)
• Decision Transformer as a Foundation Model for Partially Observable Continuous Control [5.453548045211778]
  Decision Transformer (DT) architecture is used to predict optimal action based on past observations, actions, and rewards. DT exhibits remarkable zero-shot generalization abilities for completely new tasks. These findings highlight the potential of DT as a foundational controller for general control applications.
  arXiv  Detail & Related papers  (2024-04-03T02:17:34Z)
• Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning [97.99077847606624]
  This work proposes a novel architecture that integrates a wake-up radio mechanism within a split computing system consisting of remote, wirelessly connected, NPUs. A key challenge in the design of a wake-up radio-based neuromorphic split computing system is the selection of thresholds for sensing, wake-up signal detection, and decision making.
  arXiv  Detail & Related papers  (2024-04-02T10:19:04Z)
• Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach [74.25870052841226]
  A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing, and artificial intelligence (AI) technologies to enable many connected intelligence services. Wireless systems can exploit the paradigm of semantic communication (SC) for facilitating informed decision-making under strict communication constraints. A novel framework called causal semantic communication (CSC) is proposed for DT-based wireless systems.
  arXiv  Detail & Related papers  (2023-04-25T00:15:00Z)
• A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems [35.59201763567714]
  Digital twin (DT) platforms are seen as a promising paradigm to control, monitor, and analyze software-based, "open", communication systems. A key challenge in the deployment of DT systems is to ensure that virtual control optimization, monitoring, and analysis at the DT are safe and reliable. This paper presents a general Bayesian framework with the aim of quantifying and accounting for model uncertainty at the DT.
  arXiv  Detail & Related papers  (2022-12-02T18:13:48Z)
• Using Neural Networks by Modelling Semi-Active Shock Absorber [0.0]
  A permanently increasing number of on-board automotive control systems requires new approaches to their digital mapping. Various methods applying neural networks (NN) can be good candidates for relevant digital twin (DT) tools in automotive control system design. The paper presents an approach, which demonstrates how the regression tasks can be efficiently handled by the modeling of a semi-active shock absorber.
  arXiv  Detail & Related papers  (2022-07-19T09:21:21Z)
• Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access [65.21117658030235]
  This work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS) and average training overhead. Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR.
  arXiv  Detail & Related papers  (2022-05-02T12:23:55Z)
• Multi-task Learning Approach for Modulation and Wireless Signal Classification for 5G and Beyond: Edge Deployment via Model Compression [1.218340575383456]
  Future communication networks must address the scarce spectrum to accommodate growth of heterogeneous wireless devices. We exploit the potential of deep neural networks based multi-task learning framework to simultaneously learn modulation and signal classification tasks. We provide a comprehensive heterogeneous wireless signals dataset for public use.
  arXiv  Detail & Related papers  (2022-02-26T14:51:02Z)
• UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers [108.92194081987967]
  We make the first attempt to explore a universal multi-agent reinforcement learning pipeline, designing one single architecture to fit tasks. Unlike previous RNN-based models, we utilize a transformer-based model to generate a flexible policy. The proposed model, named as Universal Policy Decoupling Transformer (UPDeT), further relaxes the action restriction and makes the multi-agent task's decision process more explainable.
  arXiv  Detail & Related papers  (2021-01-20T07:24:24Z)

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.