Poster: Cloud Computing with AI-empowered Trends in Software-Defined Radios: Challenges and Opportunities
Poster
Sharma, Ekta, Deo, Ravinesh C., Davey, Christopher P., Carter, Brad D. and Salcedo-sanz, Sancho. 2024. "Poster: Cloud Computing with AI-empowered Trends in Software-Defined Radios: Challenges and Opportunities." 2024 IEEE 25th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM 2024). Perth, Australia 04 - 07 Jun 2024 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/WoWMoM60985.2024.00054
Paper/Presentation Title | Poster: Cloud Computing with AI-empowered Trends in Software-Defined Radios: Challenges and Opportunities |
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Presentation Type | Poster |
Authors | Sharma, Ekta, Deo, Ravinesh C., Davey, Christopher P., Carter, Brad D. and Salcedo-sanz, Sancho |
Journal or Proceedings Title | Proceedings of 2024 IEEE 25th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM 2024) |
Journal Citation | pp. 298-300 |
Number of Pages | 3 |
Year | 2024 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISBN | 9798350394665 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/WoWMoM60985.2024.00054 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/10579244 |
Web Address (URL) of Conference Proceedings | https://ieeexplore.ieee.org/xpl/conhome/10579075/proceeding |
Conference/Event | 2024 IEEE 25th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM 2024) |
Event Details | 2024 IEEE 25th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM 2024) Delivery In person Event Date 04 to end of 07 Jun 2024 Event Location Perth, Australia |
Abstract | Artificial Intelligence (AI) and Software Defined Radio (SDR) are transforming the field of signal intelligence. However, the full extent of the capabilities is unknown. This poster presents a paper in development that introduces a cloud-based platform leveraging artificial intelligence to detect and apply 11 modulation schemes (8 digital and 3 analog) to complex or quadrature radio signals. The SNR values analysed range from 0.0 to 40.0, with moderate drift, slight fading, and labelled increments. A comprehensive synthetic database developed by DeepSig is used to train four AI models. These will be integrated with the Google Cloud AI platform to enhance flexibility and processing power. The system will undergo testing with an SDR platform in GNU Radio, showcasing its potential for real-world signal processing applications. Cloud-based platforms offer the adaptability and computational power needed to replace traditional computers for AI-driven signal processing. Initial results indicate successful identification and accurate modulation type detection, with convenient access to the system through internet-connected devices. © 2024 IEEE. |
Keywords | artificial intelligence; cloud computing; software-defined radio; modulation; GNU Radio; signal classification |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 461103. Deep learning |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Mathematics, Physics and Computing |
Centre for Astrophysics | |
University of Alcala, Spain |
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