  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.
