Generalized hybrid LiFi-WiFi UniPHY learning framework towards intelligent UAV-based indoor networks
Advancements in unmanned aerial vehicle (UAV) technology, along with indoor hybrid LiFi-WiFi networks (HLWN), promise the development of cost-effective, energy-efficient, adaptable, reliable, rapid, and on-demand HLWN-capable indoor flying networks (IFNs). To achieve this, a unified physical layer (...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-01-01
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| Series: | International Journal of Intelligent Networks |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666603024000277 |
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| author | Rizwana Ahmad Dil Nashin Anwar Haythem Bany Salameh Hany Elgala Moussa Ayyash Sufyan Almajali Reyad El-Khazali |
| author_facet | Rizwana Ahmad Dil Nashin Anwar Haythem Bany Salameh Hany Elgala Moussa Ayyash Sufyan Almajali Reyad El-Khazali |
| author_sort | Rizwana Ahmad |
| collection | DOAJ |
| description | Advancements in unmanned aerial vehicle (UAV) technology, along with indoor hybrid LiFi-WiFi networks (HLWN), promise the development of cost-effective, energy-efficient, adaptable, reliable, rapid, and on-demand HLWN-capable indoor flying networks (IFNs). To achieve this, a unified physical layer (UniPHY) capable of simultaneous control communication, data transfer, and sensing is crucial. However, traditional block-based decoders, designed independently for LiFi and WiFi, perform poorly in complex and hybrid LiFi-WiFi-enabled UniPHY systems. In this study, we propose an end-to-end learning framework based on convolutional neural networks (CNNs) for UniPHY, which can be trained to serve hybrid LiFi-WiFi transmissions to improve error performance and simplify UAV hardware. In this work, the performance of the proposed framework is assessed and compared with that of the conventional independent block-based communication system. Furthermore, a comprehensive summary of optimal hyper-parameters for efficient training of our learning framework has been provided. It is shown that, with optimal hyper-parameters, the proposed CNN-based framework outperforms the conventional block-based approach by providing a signal-to-noise ratio gain of approximately 7 dB for the LiFi channel and 23 dB for the WiFi channel. In addition, we evaluate the complexity and training convergence for loss and accuracy. |
| format | Article |
| id | doaj-art-214d72e58b4b4c628d87c5bf7e4c8436 |
| institution | OA Journals |
| issn | 2666-6030 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Intelligent Networks |
| spelling | doaj-art-214d72e58b4b4c628d87c5bf7e4c84362025-08-20T02:22:36ZengKeAi Communications Co., Ltd.International Journal of Intelligent Networks2666-60302024-01-01525526610.1016/j.ijin.2024.05.008Generalized hybrid LiFi-WiFi UniPHY learning framework towards intelligent UAV-based indoor networksRizwana Ahmad0Dil Nashin Anwar1Haythem Bany Salameh2Hany Elgala3Moussa Ayyash4Sufyan Almajali5Reyad El-Khazali6Electronic and Electrical Engineering Department, University of Cambridge, UKElectrical and Computer Engineering Department, University at Albany-State University of New York, USAArtificial Intelligence Research Center, Al Ain University, Al Ain, United Arab Emirates; Telecommunications Engineering Department, Yarmouk University, Irbid, Jordan; Corresponding author. Artificial Intelligence Research Center, Al Ain University, Al Ain, United Arab Emirates.Electrical and Computer Engineering Department, University at Albany-State University of New York, USADepartment of Computing Information and Technology, Chicago State University, Chicago, IL, USADepartment of Computer Science, Princess Sumaya University for Technology, Amman, JordanElectrical Engineering and Computer Science Department, Khalifa University, Abu Dhabi, United Arab EmiratesAdvancements in unmanned aerial vehicle (UAV) technology, along with indoor hybrid LiFi-WiFi networks (HLWN), promise the development of cost-effective, energy-efficient, adaptable, reliable, rapid, and on-demand HLWN-capable indoor flying networks (IFNs). To achieve this, a unified physical layer (UniPHY) capable of simultaneous control communication, data transfer, and sensing is crucial. However, traditional block-based decoders, designed independently for LiFi and WiFi, perform poorly in complex and hybrid LiFi-WiFi-enabled UniPHY systems. In this study, we propose an end-to-end learning framework based on convolutional neural networks (CNNs) for UniPHY, which can be trained to serve hybrid LiFi-WiFi transmissions to improve error performance and simplify UAV hardware. In this work, the performance of the proposed framework is assessed and compared with that of the conventional independent block-based communication system. Furthermore, a comprehensive summary of optimal hyper-parameters for efficient training of our learning framework has been provided. It is shown that, with optimal hyper-parameters, the proposed CNN-based framework outperforms the conventional block-based approach by providing a signal-to-noise ratio gain of approximately 7 dB for the LiFi channel and 23 dB for the WiFi channel. In addition, we evaluate the complexity and training convergence for loss and accuracy.http://www.sciencedirect.com/science/article/pii/S2666603024000277Unmanned aerial vehiclesVisible light communicationAuto-encodersDeep learningNeural networks |
| spellingShingle | Rizwana Ahmad Dil Nashin Anwar Haythem Bany Salameh Hany Elgala Moussa Ayyash Sufyan Almajali Reyad El-Khazali Generalized hybrid LiFi-WiFi UniPHY learning framework towards intelligent UAV-based indoor networks International Journal of Intelligent Networks Unmanned aerial vehicles Visible light communication Auto-encoders Deep learning Neural networks |
| title | Generalized hybrid LiFi-WiFi UniPHY learning framework towards intelligent UAV-based indoor networks |
| title_full | Generalized hybrid LiFi-WiFi UniPHY learning framework towards intelligent UAV-based indoor networks |
| title_fullStr | Generalized hybrid LiFi-WiFi UniPHY learning framework towards intelligent UAV-based indoor networks |
| title_full_unstemmed | Generalized hybrid LiFi-WiFi UniPHY learning framework towards intelligent UAV-based indoor networks |
| title_short | Generalized hybrid LiFi-WiFi UniPHY learning framework towards intelligent UAV-based indoor networks |
| title_sort | generalized hybrid lifi wifi uniphy learning framework towards intelligent uav based indoor networks |
| topic | Unmanned aerial vehicles Visible light communication Auto-encoders Deep learning Neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S2666603024000277 |
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