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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-sourcetechnology. By adopting the Chirp Spread Spectrum (CSS) modulation, LoRaenables low signal-to-noise ratio (SNR) communication. The standard LoRademodulation method accumulates the chirp power of the whole chirp into anenergy peak in the frequency domain. In this way, it can support communicationeven when SNR is lower than -15 dB. Beyond that, we proposed NELoRa, aneural-enhanced decoder that exploits multi-dimensional information to achievesignificant SNR gain. This paper presents the dataset used to train/testNELoRa, which includes 27,329 LoRa symbols with spreading factors from 7 to 10,for further improvement of neural-enhanced LoRa demodulation. The dataset showsthat NELoRa can achieve 1.84-2.35 dB SNR gain over the standard LoRa decoder.The dataset and codes can be found athttps://github.com/daibiaoxuwu/NeLoRa_Dataset.

NELoRa-Bench: A Benchmark for Neural-enhanced LoRa Demodulation