Latency-aware blockage prediction in vision-aided federated wireless networks
Introduction: The future wireless landscape is evolving rapidly to meet ever-increasing data requirements, which can be enabled using higher-frequency spectrums like millimetre waves (mmWaves) and terahertz (THz). However, mmWave and THztechnologies rely on line-of-sight (LOS) communication, making...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Communications and Networks |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frcmn.2023.1130844/full |
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| author | Ahsan Raza Khan Iftikhar Ahmad Lina Mohjazi Sajjad Hussain Rao Naveed Bin Rais Muhammad Ali Imran Muhammad Ali Imran Ahmed Zoha |
| author_facet | Ahsan Raza Khan Iftikhar Ahmad Lina Mohjazi Sajjad Hussain Rao Naveed Bin Rais Muhammad Ali Imran Muhammad Ali Imran Ahmed Zoha |
| author_sort | Ahsan Raza Khan |
| collection | DOAJ |
| description | Introduction: The future wireless landscape is evolving rapidly to meet ever-increasing data requirements, which can be enabled using higher-frequency spectrums like millimetre waves (mmWaves) and terahertz (THz). However, mmWave and THztechnologies rely on line-of-sight (LOS) communication, making them sensitive to sudden environmental changes and higher mobility of users, especially in urban areas.Methods: Therefore, beam blockage prediction is a critical challenge for sixth-generation (6G) wireless networks. One possible solution is to anticipate the potential change in the wireless network surroundings using multi-sensor data (wireless, vision, lidar, and GPS) with advanced deep learning (DL) and computer vision (CV) techniques. Despite numerous advantages, the fusion of deep learning,computer vision, and multi-modal data in centralised training introduces many challenges, including higher communication costs for raw data transfer, inefficient bandwidth usage and unacceptable latency. This work proposes latency-aware vision-aided federated wireless networks (VFWN) for beam blockage prediction using bimodal vision and wireless sensing data. The proposed framework usesdistributed learning on the edge nodes (EN) for data processing and model training.Results and Discussion: This involves federated learning for global model aggregation that minimizes latency and data communication cost as compared to centralised learning while achieving comparable predictive accuracy. For instance, the VFWN achieves a predictive accuracy of 98.5%, which is comparable to centralised learning with overall predictive accuracy 99%, considering that no data sharing is done. Furthermore, the proposed framework significantly reduces the communication cost by 81.31% and latency by 6.77% using real-time on device processing and inference. |
| first_indexed | 2024-04-10T15:18:27Z |
| format | Article |
| id | doaj.art-9035980f96294f4d9788778f77dfe9b2 |
| institution | Directory Open Access Journal |
| issn | 2673-530X |
| language | English |
| last_indexed | 2024-04-10T15:18:27Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Communications and Networks |
| spelling | doaj.art-9035980f96294f4d9788778f77dfe9b22023-02-14T17:58:03ZengFrontiers Media S.A.Frontiers in Communications and Networks2673-530X2023-02-01410.3389/frcmn.2023.11308441130844Latency-aware blockage prediction in vision-aided federated wireless networksAhsan Raza Khan0Iftikhar Ahmad1Lina Mohjazi2Sajjad Hussain3Rao Naveed Bin Rais4Muhammad Ali Imran5Muhammad Ali Imran6Ahmed Zoha7James Watt School of Engineering, University of Glasgow, Glasgow, United KingdomJames Watt School of Engineering, University of Glasgow, Glasgow, United KingdomJames Watt School of Engineering, University of Glasgow, Glasgow, United KingdomJames Watt School of Engineering, University of Glasgow, Glasgow, United KingdomArtificial Intelligence Research Center (AIRC), Ajman University, Ajman, United Arab EmiratesJames Watt School of Engineering, University of Glasgow, Glasgow, United KingdomArtificial Intelligence Research Center (AIRC), Ajman University, Ajman, United Arab EmiratesJames Watt School of Engineering, University of Glasgow, Glasgow, United KingdomIntroduction: The future wireless landscape is evolving rapidly to meet ever-increasing data requirements, which can be enabled using higher-frequency spectrums like millimetre waves (mmWaves) and terahertz (THz). However, mmWave and THztechnologies rely on line-of-sight (LOS) communication, making them sensitive to sudden environmental changes and higher mobility of users, especially in urban areas.Methods: Therefore, beam blockage prediction is a critical challenge for sixth-generation (6G) wireless networks. One possible solution is to anticipate the potential change in the wireless network surroundings using multi-sensor data (wireless, vision, lidar, and GPS) with advanced deep learning (DL) and computer vision (CV) techniques. Despite numerous advantages, the fusion of deep learning,computer vision, and multi-modal data in centralised training introduces many challenges, including higher communication costs for raw data transfer, inefficient bandwidth usage and unacceptable latency. This work proposes latency-aware vision-aided federated wireless networks (VFWN) for beam blockage prediction using bimodal vision and wireless sensing data. The proposed framework usesdistributed learning on the edge nodes (EN) for data processing and model training.Results and Discussion: This involves federated learning for global model aggregation that minimizes latency and data communication cost as compared to centralised learning while achieving comparable predictive accuracy. For instance, the VFWN achieves a predictive accuracy of 98.5%, which is comparable to centralised learning with overall predictive accuracy 99%, considering that no data sharing is done. Furthermore, the proposed framework significantly reduces the communication cost by 81.31% and latency by 6.77% using real-time on device processing and inference.https://www.frontiersin.org/articles/10.3389/frcmn.2023.1130844/fullblockage predictionvision-aided wireless communicationfederated learningmmWave (millimeter wave)proactive handover |
| spellingShingle | Ahsan Raza Khan Iftikhar Ahmad Lina Mohjazi Sajjad Hussain Rao Naveed Bin Rais Muhammad Ali Imran Muhammad Ali Imran Ahmed Zoha Latency-aware blockage prediction in vision-aided federated wireless networks Frontiers in Communications and Networks blockage prediction vision-aided wireless communication federated learning mmWave (millimeter wave) proactive handover |
| title | Latency-aware blockage prediction in vision-aided federated wireless networks |
| title_full | Latency-aware blockage prediction in vision-aided federated wireless networks |
| title_fullStr | Latency-aware blockage prediction in vision-aided federated wireless networks |
| title_full_unstemmed | Latency-aware blockage prediction in vision-aided federated wireless networks |
| title_short | Latency-aware blockage prediction in vision-aided federated wireless networks |
| title_sort | latency aware blockage prediction in vision aided federated wireless networks |
| topic | blockage prediction vision-aided wireless communication federated learning mmWave (millimeter wave) proactive handover |
| url | https://www.frontiersin.org/articles/10.3389/frcmn.2023.1130844/full |
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