Smart Handover with Predicted User Behavior using Convolutional Neural Networks for WiGig Systems
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914101122301952 |
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| author | Rodrigues, Tiago Koketsu Verma, Shikhar Kawamoto, Yuichi Kato, Nei Fouda, Mostafa M. Ismail, Muhammad |
| author_facet | Rodrigues, Tiago Koketsu Verma, Shikhar Kawamoto, Yuichi Kato, Nei Fouda, Mostafa M. Ismail, Muhammad |
| contents | WiGig networks and 60 GHz frequency communications have a lot of potential for commercial and personal use. They can offer extremely high transmission rates but at the cost of low range and penetration. Due to these issues, WiGig systems are unstable and need to rely on frequent handovers to maintain high-quality connections. However, this solution is problematic as it forces users into bad connections and downtime before they are switched to a better access point. In this work, we use Machine Learning to identify patterns in user behaviors and predict user actions. This prediction is used to do proactive handovers, switching users to access points with better future transmission rates and a more stable environment based on the future state of the user. Results show that not only the proposal is effective at predicting channel data, but the use of such predictions improves system performance and avoids unnecessary handovers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_15731 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Smart Handover with Predicted User Behavior using Convolutional Neural Networks for WiGig Systems Rodrigues, Tiago Koketsu Verma, Shikhar Kawamoto, Yuichi Kato, Nei Fouda, Mostafa M. Ismail, Muhammad Networking and Internet Architecture WiGig networks and 60 GHz frequency communications have a lot of potential for commercial and personal use. They can offer extremely high transmission rates but at the cost of low range and penetration. Due to these issues, WiGig systems are unstable and need to rely on frequent handovers to maintain high-quality connections. However, this solution is problematic as it forces users into bad connections and downtime before they are switched to a better access point. In this work, we use Machine Learning to identify patterns in user behaviors and predict user actions. This prediction is used to do proactive handovers, switching users to access points with better future transmission rates and a more stable environment based on the future state of the user. Results show that not only the proposal is effective at predicting channel data, but the use of such predictions improves system performance and avoids unnecessary handovers. |
| title | Smart Handover with Predicted User Behavior using Convolutional Neural Networks for WiGig Systems |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2303.15731 |