TinyML-Based Adaptive Pulse Shaping for Edge Intelligence in IoT/IIoT
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866913881225428992 |
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| author | Ali, Afan |
| author_facet | Ali, Afan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05789 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TinyML-Based Adaptive Pulse Shaping for Edge Intelligence in IoT/IIoT Ali, Afan Signal Processing 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. |
| title | TinyML-Based Adaptive Pulse Shaping for Edge Intelligence in IoT/IIoT |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2506.05789 |