File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/TWC.2024.3512589
- Scopus: eid_2-s2.0-86000433995
- Find via

Supplementary
-
Citations:
- Scopus: 0
- Appears in Collections:
Article: DEEP-IoT: Downlink-Enhanced Efficient-Power Internet of Things
| Title | DEEP-IoT: Downlink-Enhanced Efficient-Power Internet of Things |
|---|---|
| Authors | |
| Keywords | energy efficiency feedback channel codes IoT SC-FDMA subcarrier allocation |
| Issue Date | 2025 |
| Citation | IEEE Transactions on Wireless Communications, 2025, v. 24, n. 2, p. 1722-1736 How to Cite? |
| Abstract | 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. |
| Persistent Identifier | http://hdl.handle.net/10722/363700 |
| ISSN | 2023 Impact Factor: 8.9 2023 SCImago Journal Rankings: 5.371 |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Shao, Yulin | - |
| dc.date.accessioned | 2025-10-10T07:48:40Z | - |
| dc.date.available | 2025-10-10T07:48:40Z | - |
| dc.date.issued | 2025 | - |
| dc.identifier.citation | IEEE Transactions on Wireless Communications, 2025, v. 24, n. 2, p. 1722-1736 | - |
| dc.identifier.issn | 1536-1276 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/363700 | - |
| dc.description.abstract | 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. | - |
| dc.language | eng | - |
| dc.relation.ispartof | IEEE Transactions on Wireless Communications | - |
| dc.subject | energy efficiency | - |
| dc.subject | feedback channel codes | - |
| dc.subject | IoT | - |
| dc.subject | SC-FDMA | - |
| dc.subject | subcarrier allocation | - |
| dc.title | DEEP-IoT: Downlink-Enhanced Efficient-Power Internet of Things | - |
| dc.type | Article | - |
| dc.description.nature | link_to_subscribed_fulltext | - |
| dc.identifier.doi | 10.1109/TWC.2024.3512589 | - |
| dc.identifier.scopus | eid_2-s2.0-86000433995 | - |
| dc.identifier.volume | 24 | - |
| dc.identifier.issue | 2 | - |
| dc.identifier.spage | 1722 | - |
| dc.identifier.epage | 1736 | - |
| dc.identifier.eissn | 1558-2248 | - |
