ViWi Vision-Aided mmWave Beam Tracking: Dataset, Task, and Baseline Solutions

Alrabeiah, Muhammad; Booth, Jayden Charles; Hredzak, Andrew; Alkhateeb, Ahmed

doi:10.48550/arxiv.2002.02445

Cited by 13 publications

(31 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3) Performance: Following the evaluation in [6], the performance of our proposed methods is evaluated on the validation set with the same metrics, which are exponential decay score and top-1 accuracy. Their detailed expression can be explained by Equations 6 and 7 in [6]. Our results are listed in Table I in which the baseline method in [6] is considered for com parson purposes.…”

Section: Methodsmentioning

confidence: 99%

“…Using the provided Matlab script, users can get the user's location and channel information in each image from the raw wireless data. Later the same authors built the second version dataset called ViWi Vision-Aided Millimeter-Wave Beam Tracking (ViWi-BT) [6] and post it for the machine learning competition at IEEE International Conference on Communications 2020. This dataset contains images captured by the co-located cameras and mmWave MIMO beam indices under a predefined codebook.…”

Section: An Overview Of Related Workmentioning

confidence: 99%

“…Based on the firstversion ViWi dataset, [7] proposed a modified ResNet18 model to conduct beam and blockage prediction from the images and channel information. Based on the second-version ViWi-BT dataset, the authors of [6] provided a baseline method without the images and only with the beam indices. They believe that they can get better performance if they leverage both of these kinds of data.…”

Section: An Overview Of Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Applying Deep-Learning-Based Computer Vision to Wireless Communications: Methodologies, Opportunities, and Challenges

Tian¹,

Pan²,

Alouini³

2020

Preprint

View full text Add to dashboard Cite

Section: Methodsmentioning

confidence: 99%

Section: An Overview Of Related Workmentioning

confidence: 99%

Section: An Overview Of Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Applying Deep-Learning-Based Computer Vision to Wireless Communications: Methodologies, Opportunities, and Challenges

Tian¹,

Pan²,

Alouini³

2020

Preprint

View full text Add to dashboard Cite

“…• Based on the ViWi data-generation framework [21], the ViWi-BT challenge scenario [24] has been expanded to generate two datasets, namely the blockage-prediction and objectdetection datasets. The former is an extension of the ViWi-BT challenge dataset.…”

Section: B Contributionmentioning

confidence: 99%

Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoff

Charan¹,

Alrabeiah²,

Alkhateeb³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

The sensitivity to blockages is a key challenge for the high-frequency (5G millimeter wave and 6G sub-terahertz) wireless networks. Since these networks mainly rely on line-of-sight (LOS) links, sudden link blockages highly threaten the reliability of the networks. Further, when the LOS link is blocked, the network typically needs to hand off the user to another LOS basestation, which may incur critical time latency, especially if a search over a large codebook of narrow beams is needed. A promising way to tackle the reliability and latency challenges lies in enabling proaction in wireless networks. Proaction basically allows the network to anticipate blockages, especially dynamic blockages, and initiate user hand-off beforehand. This paper presents a complete machine learning framework for enabling proaction in wireless networks relying on visual data captured, for example, by RGB cameras deployed at the base stations. In particular, the paper proposes a vision-aided wireless communication solution that utilizes bimodal machine learning to perform proactive blockage prediction and user hand-off. The bedrock of this solution is a deep learning algorithm that learns from visual and wireless data how to predict incoming blockages. The predictions of this algorithm are used by the wireless network to proactively initiate hand-off decisions and avoid any unnecessary latency. The algorithm is developed on a vision-wireless dataset generated using the ViWi data-generation framework. Experimental results on two basestations with different cameras indicate that the algorithm is capable of accurately detecting incoming blockages more than ∼ 90% of the time. Such blockage prediction ability is directly reflected in the accuracy of proactive hand-off, which also approaches 87%. This highlights a promising direction for enabling high reliability and low latency in future wireless networks.

show abstract

“…The dataset provides co-existing wireless and visual data. The dataset is an extension of the ViWi-BT challenge dataset presented in [17]. It comprises sequences of RGB frames, beam indices, and user link statuses.…”

Section: A Datasetmentioning

confidence: 99%

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

Charan¹,

Alrabeiah²,

Alkhateeb³

2020

Preprint

Self Cite

View full text Add to dashboard Cite

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 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.

show abstract

ViWi Vision-Aided mmWave Beam Tracking: Dataset, Task, and Baseline Solutions

Cited by 13 publications

References 15 publications

Applying Deep-Learning-Based Computer Vision to Wireless Communications: Methodologies, Opportunities, and Challenges

Applying Deep-Learning-Based Computer Vision to Wireless Communications: Methodologies, Opportunities, and Challenges

Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoff

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

Contact Info

Product

Resources

About