Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network

Salehi, Batool; Reus-Muns, Guillem; Roy, Debashri; Wang, Zifeng; Jian, Tong; Dy, Jennifer G.; Ioannidis, Stratis; Chowdhury, Kaushik R.

doi:10.1109/tvt.2022.3170733

Cited by 31 publications

(21 citation statements)

References 46 publications

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“…The following study cited in [ 5 ] used both GPS and LIDAR data to improve the prediction rate of beam selection on top-10 accuracy. This analysis managed to achieve a top-10 accuracy of 91.11% with S008.…”

Section: Related Workmentioning

confidence: 99%

“…In [ 5 ], the authors designed deep learning architectures that can predict a set of top-K beam pairs with the assistance of non-RF sensor data such as GPS, camera, and LiDAR, improving the prediction accuracy by 3.32 to 43.9%. They also proposed that a fusion network exhibits 20–22% improvement in top-10 accuracy when comparing their technique against other state-of-the-art ones.…”

Section: Related Workmentioning

confidence: 99%

“…It is well-known that communication in the mmWave band faces several difficulties because of high attenuation and penetration loss. These problems can be overcome by phased arrays with directional beamforming by concentrating RF energy at the receiver [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

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Beam-Selection for 5G/B5G Networks Using Machine Learning: A Comparative Study

Chatzoglou

Goudos

2023

Sensors

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A challenging problem in millimeter wave (mmWave) communications for the fifth generation of cellular communications and beyond (5G/B5G) is the beam selection problem. This is due to severe attenuation and penetration losses that are inherent in the mmWave band. Thus, the beam selection problem for mmWave links in a vehicular scenario can be solved as an exhaustive search among all candidate beam pairs. However, this approach cannot be assuredly completed within short contact times. On the other hand, machine learning (ML) has the potential to significantly advance 5G/B5G technology, as evidenced by the growing complexity of constructing cellular networks. In this work, we perform a comparative study of using different ML methods to solve the beam selection problem. We use a common dataset for this scenario found in the literature. We increase the accuracy of these results by approximately 30%. Moreover, we extend the given dataset by producing additional synthetic data. We apply ensemble learning techniques and obtain results with about 94% accuracy. The novelty of our work lies in the fact that we improve the existing dataset by adding more synthetic data and by designing a custom ensemble learning method for the problem at hand.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Beam-Selection for 5G/B5G Networks Using Machine Learning: A Comparative Study

Chatzoglou

Goudos

2023

Sensors

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“…The authors gratefully acknowledge the funding from the US National Science Foundation (grant CNS-2112471) • ML-related Vulnerabilities: Deep neural networks (DNNs) is a popular branch of ML, which minimizes the need for domain knowledge for inference tasks. While it has seen great success in the field of computer vision, DNNs have also been used earlier for RF fingerprinting [5], modulation classification [6], beam selection [7], among others. However, DNNs are also vulnerable to attacks that are being actively studied in the emerging field of adversarial machine learning [8].…”

Section: Introductionmentioning

confidence: 99%

Wireless Virtualization Architecture: Wireless Networking for Internet of Things

Sapavath

Rawat

2020

IEEE Internet Things J.

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Open Radio Access Network (O-RAN) is considered as a major step in the evolution of next-generation cellular networks given its support for open interfaces and utilization of artificial intelligence (AI) into the deployment, operation, and maintenance of RAN. However, due to the openness of the O-RAN architecture, such AI models are inherently vulnerable to various adversarial machine learning (ML) attacks, i.e., adversarial attacks which correspond to slight manipulation of the input to the ML model. In this work, we showcase the vulnerability of an example ML model used in O-RAN, and experimentally deploy it in the near-real time (near-RT) RAN intelligent controller (RIC). Our ML-based interference classifier xApp (extensible application in near-RT RIC) tries to classify the type of interference to mitigate the interference effect on the O-RAN system. We demonstrate the first-ever scenario of how such an xApp can be impacted through an adversarial attack by manipulating the data stored in a shared database inside the near-RT RIC. Through a rigorous performance analysis deployed on a laboratory O-RAN testbed, we evaluate the performance in terms of capacity and the prediction accuracy of the interference classifier xApp using both clean and perturbed data. We show that even small adversarial attacks can significantly decrease the accuracy of ML application in near-RT RIC, which can directly impact the performance of the entire O-RAN deployment.

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“…In this regard, we propose a two-stage deep CNN architecture to properly map input images to the best beam configuration, which maximizes the SNR at the receiver. We design a testbed to validate our proposed method using National Instruments mmWave Transceiver [4] and two cameras. Moreover, we configure our setup to support simultaneous beam alignment between transmitter and receiver and demonstrate that our proposed approach outperforms the exhaustive beam sweeping (suggested by 802.11ad standard) with 93% reduction in the time required for beam initialization.…”

Section: Organization and Contributions Of The Dissertationmentioning

confidence: 99%

Leveraging deep learning on multimodal sensor data for wireless communication: from mmWave beamforming to digital twins

Salehihikouei

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Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network

Cited by 31 publications

References 46 publications

Beam-Selection for 5G/B5G Networks Using Machine Learning: A Comparative Study

Beam-Selection for 5G/B5G Networks Using Machine Learning: A Comparative Study

Wireless Virtualization Architecture: Wireless Networking for Internet of Things

Leveraging deep learning on multimodal sensor data for wireless communication: from mmWave beamforming to digital twins

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