Vision-Assisted Digital Twin Creation for mmWave Beam Management
- URL: http://arxiv.org/abs/2401.17781v1
- Date: Wed, 31 Jan 2024 12:23:55 GMT
- Title: Vision-Assisted Digital Twin Creation for mmWave Beam Management
- Authors: Maximilian Arnold, Bence Major, Fabio Valerio Massoli, Joseph B.
Soriaga, Arash Behboodi
- Abstract summary: We propose a practical Digital Twin creation pipeline and a channel simulator, that relies only on a single mounted camera and position information.
We demonstrate the performance benefits compared to methods that do not explicitly model the 3D environment, on downstream sub-tasks in beam acquisition.
- Score: 9.608394183713717
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In the context of communication networks, digital twin technology provides a
means to replicate the radio frequency (RF) propagation environment as well as
the system behaviour, allowing for a way to optimize the performance of a
deployed system based on simulations. One of the key challenges in the
application of Digital Twin technology to mmWave systems is the prevalent
channel simulators' stringent requirements on the accuracy of the 3D Digital
Twin, reducing the feasibility of the technology in real applications. We
propose a practical Digital Twin creation pipeline and a channel simulator,
that relies only on a single mounted camera and position information. We
demonstrate the performance benefits compared to methods that do not explicitly
model the 3D environment, on downstream sub-tasks in beam acquisition, using
the real-world dataset of the DeepSense6G challenge
Related papers
- WiLoR: End-to-end 3D Hand Localization and Reconstruction in-the-wild [53.288327629960364]
We present a data-driven pipeline for efficient multi-hand reconstruction in the wild.
The proposed pipeline is composed of two components: a real-time fully convolutional hand localization and a high-fidelity transformer-based 3D hand reconstruction model.
Our approach outperforms previous methods in both efficiency and accuracy on popular 2D and 3D benchmarks.
arXiv Detail & Related papers (2024-09-18T18:46:51Z) - Automatic AI Model Selection for Wireless Systems: Online Learning via Digital Twinning [50.332027356848094]
AI-based applications are deployed at intelligent controllers to carry out functionalities like scheduling or power control.
The mapping between context and AI model parameters is ideally done in a zero-shot fashion.
This paper introduces a general methodology for the online optimization of AMS mappings.
arXiv Detail & Related papers (2024-06-22T11:17:50Z) - Federated Multi-View Synthesizing for Metaverse [52.59476179535153]
The metaverse is expected to provide immersive entertainment, education, and business applications.
Virtual reality (VR) transmission over wireless networks is data- and computation-intensive.
We have developed a novel multi-view synthesizing framework that can efficiently provide synthesizing, storage, and communication resources for wireless content delivery in the metaverse.
arXiv Detail & Related papers (2023-12-18T13:51:56Z) - StableDreamer: Taming Noisy Score Distillation Sampling for Text-to-3D [88.66678730537777]
We present StableDreamer, a methodology incorporating three advances.
First, we formalize the equivalence of the SDS generative prior and a simple supervised L2 reconstruction loss.
Second, our analysis shows that while image-space diffusion contributes to geometric precision, latent-space diffusion is crucial for vivid color rendition.
arXiv Detail & Related papers (2023-12-02T02:27:58Z) - Hybrid Knowledge-Data Driven Channel Semantic Acquisition and
Beamforming for Cell-Free Massive MIMO [6.010360758759109]
This paper focuses on advancing outdoor wireless systems to better support ubiquitous extended reality (XR) applications.
We propose a hybrid knowledge-data driven method for channel semantic acquisition and multi-user beamforming in cell-free massive multiple-input multiple-output (MIMO) systems.
arXiv Detail & Related papers (2023-07-06T15:35:55Z) - Digital Twin-Enhanced Wireless Indoor Navigation: Achieving Efficient Environment Sensing with Zero-Shot Reinforcement Learning [21.79206567364126]
Millimeter-wave (mmWave) communication is a vital component of future mobile networks, making it ideal for indoor navigation in complex environments.
Traditional physics-based methods, such as following the angle of arrival (AoA), often fall short in complex scenarios.
We propose a Physics-Informed Reinforcement Learning (PIRL) approach that leverages the physical insights provided by digital twins to shape the reinforcement learning (RL) reward function.
arXiv Detail & Related papers (2023-06-11T20:33:22Z) - A Multi-Modal Simulation Framework to Enable Digital Twin-based V2X Communications in Dynamic Environments [10.652127049174883]
Digital Twins (DTs) for physical wireless environments have been recently proposed as accurate virtual representations of the propagation environment.
We propose a novel data-driven workflow for the creation of the DT of a Vehicle-to-Everything (V2X) communication scenario.
We showcase the proposed framework on the DT-aided blockage handover task for V2X link restoration.
arXiv Detail & Related papers (2023-03-13T09:31:20Z) - Simulation of machine learning-based 6G systems in virtual worlds [0.14072064932290224]
6G systems will not only support use cases that rely on virtual worlds but also benefit from their rich contextual information to improve performance and reduce communication overhead.
This paper focuses on the simulation of 6G systems that rely on a 3D representation of the environment, as captured by cameras and other sensors.
arXiv Detail & Related papers (2022-04-15T15:42:44Z) - Dual-view Snapshot Compressive Imaging via Optical Flow Aided Recurrent
Neural Network [14.796204921975733]
Dual-view snapshot compressive imaging (SCI) aims to capture videos from two field-of-views (FoVs) in a single snapshot.
It is challenging for existing model-based decoding algorithms to reconstruct each individual scene.
We propose an optical flow-aided recurrent neural network for dual video SCI systems, which provides high-quality decoding in seconds.
arXiv Detail & Related papers (2021-09-11T14:24:44Z) - Energy-Efficient Model Compression and Splitting for Collaborative
Inference Over Time-Varying Channels [52.60092598312894]
We propose a technique to reduce the total energy bill at the edge device by utilizing model compression and time-varying model split between the edge and remote nodes.
Our proposed solution results in minimal energy consumption and $CO$ emission compared to the considered baselines.
arXiv Detail & Related papers (2021-06-02T07:36:27Z) - Applying Deep-Learning-Based Computer Vision to Wireless Communications:
Methodologies, Opportunities, and Challenges [100.45137961106069]
Deep learning (DL) has seen great success in the computer vision (CV) field.
This article introduces ideas about applying DL-based CV in wireless communications.
arXiv Detail & Related papers (2020-06-10T11:37:49Z)
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.