Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2511.18484 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866911283331203072 |
|---|---|
| author | Chen, Weiwei Xiao, Huaxuan Zhang, Jiefeng Xia, Xianjin Wang, Shuai Deng, Xianjun Zeng, Dan |
| author_facet | Chen, Weiwei Xiao, Huaxuan Zhang, Jiefeng Xia, Xianjin Wang, Shuai Deng, Xianjun Zeng, Dan |
| contents | LoRa has become a cornerstone for city-wide IoT applications due to its long-range, low-power communication. It achieves extended transmission by spreading symbols over multiple samples, with redundancy controlled by the Spreading Factor (SF), and further error resilience provided by Forward Error Correction (FEC). However, practical limits on SF and the separation between signal-level demodulation and coding-level error correction in conventional LoRa PHY leave it vulnerable under extremely weak signals - common in city-scale deployments. To address this, we present SFusion, a software-based coding framework that jointly leverages signal-level aggregation and coding-level redundancy to enhance LoRa's robustness. When signals fall below the decodable threshold, SFusion encodes a quasi-SF(k +m) symbol using 2^m SFk symbols to boost processing gain through energy accumulation. Once partial decoding becomes feasible with energy aggregation, an opportunistic decoding strategy directly combines IQ signals across symbols to recover errors. Extensive evaluations show that SFusion achieves up to 15dB gain over SF12 and up to 13dB improvement over state-of-the-art solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18484 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SFusion: Energy and Coding Fusion for Ultra-Robust Low-SNR LoRa Networks Chen, Weiwei Xiao, Huaxuan Zhang, Jiefeng Xia, Xianjin Wang, Shuai Deng, Xianjun Zeng, Dan Networking and Internet Architecture LoRa has become a cornerstone for city-wide IoT applications due to its long-range, low-power communication. It achieves extended transmission by spreading symbols over multiple samples, with redundancy controlled by the Spreading Factor (SF), and further error resilience provided by Forward Error Correction (FEC). However, practical limits on SF and the separation between signal-level demodulation and coding-level error correction in conventional LoRa PHY leave it vulnerable under extremely weak signals - common in city-scale deployments. To address this, we present SFusion, a software-based coding framework that jointly leverages signal-level aggregation and coding-level redundancy to enhance LoRa's robustness. When signals fall below the decodable threshold, SFusion encodes a quasi-SF(k +m) symbol using 2^m SFk symbols to boost processing gain through energy accumulation. Once partial decoding becomes feasible with energy aggregation, an opportunistic decoding strategy directly combines IQ signals across symbols to recover errors. Extensive evaluations show that SFusion achieves up to 15dB gain over SF12 and up to 13dB improvement over state-of-the-art solutions. |
| title | SFusion: Energy and Coding Fusion for Ultra-Robust Low-SNR LoRa Networks |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2511.18484 |