Vision-Aided Dynamic Blockage Prediction for 6G Wireless Communication Networks

• URL: http://arxiv.org/abs/2006.09902v2
• Date: Thu, 18 Jun 2020 03:09:15 GMT
• Title: Vision-Aided Dynamic Blockage Prediction for 6G Wireless Communication Networks
• Authors: Gouranga Charan, Muhammad Alrabeiah, and Ahmed Alkhateeb
• Abstract summary: This paper proposes a novel solution that proactively predicts textitdynamic link blockages. It learns from observed sequences of RGB images and beamforming vectors how to predict possible future link blockages. It scores a link-blockage prediction accuracy in the neighborhood of 86%, a performance that is unlikely to be matched without utilizing visual data.
• Score: 11.626009272815816
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unlocking the full potential of millimeter-wave and sub-terahertz wireless communication networks hinges on realizing unprecedented low-latency and high-reliability requirements. The challenge in meeting those requirements lies partly in the sensitivity of signals in the millimeter-wave and sub-terahertz frequency ranges to blockages. One promising way to tackle that challenge is to help a wireless network develop a sense of its surrounding using machine learning. This paper attempts to do that by utilizing deep learning and computer vision. It proposes a novel solution that proactively predicts \textit{dynamic} link blockages. More specifically, it develops a deep neural network architecture that learns from observed sequences of RGB images and beamforming vectors how to predict possible future link blockages. The proposed architecture is evaluated on a publicly available dataset that represents a synthetic dynamic communication scenario with multiple moving users and blockages. It scores a link-blockage prediction accuracy in the neighborhood of 86\%, a performance that is unlikely to be matched without utilizing visual data.

Related papers

• AI Flow at the Network Edge [58.31090055138711]
  AI Flow is a framework that streamlines the inference process by jointly leveraging the heterogeneous resources available across devices, edge nodes, and cloud servers. This article serves as a position paper for identifying the motivation, challenges, and principles of AI Flow.
  arXiv  Detail & Related papers  (2024-11-19T12:51:17Z)
• ViT LoS V2X: Vision Transformers for Environment-aware LoS Blockage Prediction for 6G Vehicular Networks [20.953587995374168]
  We propose a Deep Learning-based approach that combines Convolutional Neural Networks (CNNs) and customized Vision Transformers (ViTs) Our method capitalizes on the synergistic strengths of CNNs and ViTs to extract features from time-series multimodal data. Our results show that the proposed approach achieves high accuracy and outperforms state-of-the-art solutions, achieving more than $95%$ accurate predictions.
  arXiv  Detail & Related papers  (2024-06-27T01:38:09Z)
• Physical-Layer Semantic-Aware Network for Zero-Shot Wireless Sensing [74.12670841657038]
  Device-free wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications. Data heterogeneity in wireless signals and data privacy regulation of distributed sensing have been considered as the major challenges that hinder the wide applications of wireless sensing in large area networking systems. We propose a novel zero-shot wireless sensing solution that allows models constructed in one or a limited number of locations to be directly transferred to other locations without any labeled data.
  arXiv  Detail & Related papers  (2023-12-08T13:50:30Z)
• Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks [63.246437631458356]
  Next-generation wireless networks (e.g., 6G) will be artificial intelligence (AI)-native. This article introduces a novel framework for building AI-native wireless networks; grounded in the emerging field of causal reasoning. We highlight several wireless networking challenges that can be addressed by causal discovery and representation.
  arXiv  Detail & Related papers  (2023-09-23T00:05:39Z)
• Artificial Intelligence Empowered Multiple Access for Ultra Reliable and Low Latency THz Wireless Networks [76.89730672544216]
  Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era. To satisfy the ultra-reliability and low-latency demands of several B5G applications, novel mobility management approaches are required. This article presents a holistic MAC layer approach that enables intelligent user association and resource allocation, as well as flexible and adaptive mobility management.
  arXiv  Detail & Related papers  (2022-08-17T03:00:24Z)
• Neuro-Symbolic Artificial Intelligence (AI) for Intent based Semantic Communication [85.06664206117088]
  6G networks must consider semantics and effectiveness (at end-user) of the data transmission. NeSy AI is proposed as a pillar for learning causal structure behind the observed data. GFlowNet is leveraged for the first time in a wireless system to learn the probabilistic structure which generates the data.
  arXiv  Detail & Related papers  (2022-05-22T07:11:57Z)
• Computer Vision Aided Blockage Prediction in Real-World Millimeter Wave Deployments [11.842197872454848]
  This paper develops a computer vision based solution that processes the visual data captured by a camera installed at the infrastructure node. Based on the adopted real-world dataset, the developed solution achieves $approx 90%$ accuracy in predicting blockages happening within the future.
  arXiv  Detail & Related papers  (2022-03-03T18:38:10Z)
• Can one hear the shape of a neural network?: Snooping the GPU via Magnetic Side Channel [42.75879156429477]
  We explore the vulnerability of neural networks deployed as black boxes across accelerated hardware through electromagnetic side channels. The attack acquires the magnetic signal for one query with unknown input values, but known input dimensions. We demonstrate the potential accuracy of this side channel attack in recovering the details for a broad range of network architectures.
  arXiv  Detail & Related papers  (2021-09-15T16:00:05Z)
• MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking [72.65494220685525]
  We propose a new dynamic modality-aware filter generation module (named MFGNet) to boost the message communication between visible and thermal data. We generate dynamic modality-aware filters with two independent networks. The visible and thermal filters will be used to conduct a dynamic convolutional operation on their corresponding input feature maps respectively. To address issues caused by heavy occlusion, fast motion, and out-of-view, we propose to conduct a joint local and global search by exploiting a new direction-aware target-driven attention mechanism.
  arXiv  Detail & Related papers  (2021-07-22T03:10:51Z)
• Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoff [15.682727572668826]
  The sensitivity to blockages is a key challenge for the high-frequency (5G millimeter wave and 6G sub-terahertz) wireless networks. A promising way to tackle the reliability and latency challenges lies in enabling proaction in wireless networks. This paper proposes a vision-aided wireless communication solution that utilizes bimodal machine learning to perform proactive blockage prediction and user hand-off.
  arXiv  Detail & Related papers  (2021-02-18T18:12:20Z)
• Deep Learning for Moving Blockage Prediction using Real Millimeter Wave Measurements [18.365889583730507]
  Millimeter wave (mmWave) communication is a key component of 5G and beyond. A sudden blockage in the line of sight link leads to abrupt disconnection, which affects the reliability of the network. We propose a machine learning algorithm learning to predict future blockages by observing what we refer to as the pre-blockage signature.
  arXiv  Detail & Related papers  (2021-01-18T05:34:37Z)

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.