Abstract
Edge intelligence in IoT and IIoT demands lightweight algorithms for data processing on resource-constrained devices. This paper introduces a novel adaptive pulse shape filter based on TinyML for PAPR and SER optimization on edge devices used in uplink IoT communication. Implemented on IoT nodes such as sensors, our pruned neural network provides up to 2 dB PAPR saving over root-raised-cosine (RRC) filters. Mass simulations validate its efficacy in DFT-s-OFDM systems and offer an energy-efficient and scalable solution for IoT/IIoT use cases such as smart factories and rural connectivity.
| Original language | English |
|---|---|
| Article number | e70060 |
| Journal | Internet Technology Letters |
| Volume | 8 |
| Issue number | 5 |
| DOIs | |
| State | Published - 1 Sep 2025 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2025 John Wiley & Sons Ltd.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 7 Affordable and Clean Energy -
SDG 9 Industry, Innovation, and Infrastructure
Keywords
- IIoT
- IoT
- PAPR
- TinyML
- edge intelligence
- neural networks
ASJC Scopus subject areas
- Software
- Information Systems
- Computer Networks and Communications
- Artificial Intelligence
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