Low-Power Wide-Area Networks (LPWANs) are an emerging Internet-of-Things (IoT) paradigm marked by low-power and long-distance communication. Among them, LoRa is widely deployed for its unique characteristics and open-source technology. By adopting the Chirp Spread Spectrum (CSS) modulation, LoRa enables low signal-to-noise ratio (SNR) communication. The standard LoRa demodulation method accumulates the chirp power of the whole chirp into an energy peak in the frequency domain. In this way, it can support communication even when SNR is lower than -15 dB. Beyond that, we proposed NELoRa, a neural-enhanced decoder that exploits multi-dimensional information to achieve significant SNR gain. This paper presents the dataset used to train/test NELoRa, which includes 27,329 LoRa symbols with spreading factors from 7 to 10, for further improvement of neural-enhanced LoRa demodulation. The dataset shows that NELoRa can achieve 1.84-2.35 dB SNR gain over the standard LoRa decoder. The dataset and codes can be found at https://github.com/daibiaoxuwu/NeLoRa_Dataset.

翻译：低功耗广域网（LPWANs）是一种新兴的物联网（IoT）范式，其特点在于低功耗和远距离通信。其中，LoRa因其独特特性和开源技术而被广泛部署。通过采用啁啾扩频（CSS）调制，LoRa实现了低信噪比（SNR）通信。标准LoRa解调方法将整个啁啾信号的功率累积为频域中的能量峰值，从而能够在SNR低于-15 dB时支持通信。在此基础上，我们提出了NELoRa，一种利用多维信息获得显著SNR增益的神经增强解码器。本文介绍了用于训练/测试NELoRa的数据集，其中包含27,329个扩频因子从7到10的LoRa符号，旨在进一步改进神经增强LoRa解调。数据集表明，NELoRa相对于标准LoRa解码器可实现1.84-2.35 dB的SNR增益。数据集和代码可在https://github.com/daibiaoxuwu/NeLoRa_Dataset获取。