Dataset with IQ signals captured from multiple Sub-GHz technologies (Sigfox, LoRA, IEEE 802.15.4g, IEEE 802.15.4 SUN-OFDM, IEEE 802.11ah)
We provide a dataset with IQ signals captured from multiple Sub-GHz technologies. Specifically, the dataset targets wireless technology recognition (machine learning) algorithms for enabling cognitive wireless networks. The Sub-GHz technologies include Sigfox, LoRA, IEEE 802.15.4g, IEEE 802.15.4 SUN-OFDM and IEEE 802.11ah. Additionally, we added a noise signal class for allowing detection of signal abscence.
The dataset was captured using a RTL-SDR at a sampling rate of 2.048 MHz using coaxial cables. Two center frequencies (864.0 MHz and 867.4 MHz) were considered to cover all considered channels of the wireless Sub-GHz technologies. The following settings for the various technologies have been considered:
| Technology | Center frequency | Bandwidth | Modulation / setting |
|---|---|---|---|
| LoRa | 868.1 MHz | 125 MHz | Spread spectrum SF 7 |
| 868.1 MHz | 125 MHz | Spread spectrum SF 12 | |
| Sigfox | 868.2 MHz | 100 Hz | BPSK (400 chan.) |
| IEEE 802.11ah | 863.5 MHz | 1 MHz | MCS 0, 10 (BPSK) and 7 (64-QAM) |
| 864.0 MHz | 2 MHz | MCS 0 (BPSK) and 7 (64-QAM) | |
| 864.5 MHz | 1 MHz | MCS 0, 10 (BPSK) and 7 (64-QAM) | |
| 866.0 MHz | 2 MHz | MCS 0 (BPSK) and 7 (64-QAM) | |
| IEEE 802.15.4 SUN-FSK | 868.1 MHz | 200 KHz | BFSK |
| IEEE 802.15.4 SUN-OFDM | 863.625 MHz | 1.2 MHz | MCS 2 (OQPSK) |
| 863.425 MHz | 800 KHz | MCS 2 (OQPSK) | |
| 863.225 MHz | 400 KHz | MCS 2 (OQPSK) | |
| 863.125 MHz | 200 KHz | MCS 2 (OQPSK) | |
| 863.125 MHz | 200 KHz | MCS 6 (16-QAM) |
The measurement setup was captured using a mobile setup, as shown in the picture below.
More information and results with our dataset can be found in [1].
Please always refer to our publication [1] when using our dataset.
The dataset can be downloaded here.
A subset of the above technologies is also available here. More information and results with this dataset can be found in the publications [2] and [3].
[1] Fontaine, J., Shahid, A., Elsas, R., Seferagic, A., Moerman, I., & De Poorter, E. (2020, November). Multi-band sub-GHz technology recognition on NVIDIA’s Jetson Nano. In 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall) (pp. 1-7). IEEE.
[2] Shahid, A., Fontaine, J., Camelo, M., Haxhibeqiri, J., Saelens, M., Khan, Z., ... & De Poorter, E. (2019, June). A convolutional neural network approach for classification of lpwan technologies: Sigfox, lora and ieee 802.15. 4g. In 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) (pp. 1-8). IEEE.
[3] Shahid, A., Fontaine, J., Haxhibeqiri, J., Saelens, M., Khan, Z., Moerman, I., & De Poorter, E. (2019, April). Demo abstract: Identification of lpwan technologies using convolutional neural networks. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 991-992). IEEE.
If you need any further details about the dataset, then you can contact at jaron.fontaine@ugent.be or adnan.shahid@ugent.be