DEEP-IoT: Downlink-Enhanced Efficient-Power Internet of Things
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arXiv
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910698469064704 |
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| author | Shao, Yulin |
| author_facet | Shao, Yulin |
| contents | At the heart of the Internet of Things (IoT) -- a domain witnessing explosive growth -- the imperative for energy efficiency and the extension of device lifespans has never been more pressing. This paper presents DEEP-IoT, an innovative communication paradigm poised to redefine how IoT devices communicate. Through a pioneering feedback channel coding strategy, DEEP-IoT challenges and transforms the traditional transmitter (IoT devices)-centric communication model to one where the receiver (the access point) play a pivotal role, thereby cutting down energy use and boosting device longevity. We not only conceptualize DEEP-IoT but also actualize it by integrating deep learning-enhanced feedback channel codes within a narrow-band system. Simulation results show a significant enhancement in the operational lifespan of IoT cells -- surpassing traditional systems using Turbo and Polar codes by up to 52.71%. This leap signifies a paradigm shift in IoT communications, setting the stage for a future where IoT devices boast unprecedented efficiency and durability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00321 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DEEP-IoT: Downlink-Enhanced Efficient-Power Internet of Things Shao, Yulin Information Theory Machine Learning Systems and Control Signal Processing At the heart of the Internet of Things (IoT) -- a domain witnessing explosive growth -- the imperative for energy efficiency and the extension of device lifespans has never been more pressing. This paper presents DEEP-IoT, an innovative communication paradigm poised to redefine how IoT devices communicate. Through a pioneering feedback channel coding strategy, DEEP-IoT challenges and transforms the traditional transmitter (IoT devices)-centric communication model to one where the receiver (the access point) play a pivotal role, thereby cutting down energy use and boosting device longevity. We not only conceptualize DEEP-IoT but also actualize it by integrating deep learning-enhanced feedback channel codes within a narrow-band system. Simulation results show a significant enhancement in the operational lifespan of IoT cells -- surpassing traditional systems using Turbo and Polar codes by up to 52.71%. This leap signifies a paradigm shift in IoT communications, setting the stage for a future where IoT devices boast unprecedented efficiency and durability. |
| title | DEEP-IoT: Downlink-Enhanced Efficient-Power Internet of Things |
| topic | Information Theory Machine Learning Systems and Control Signal Processing |
| url | https://arxiv.org/abs/2403.00321 |