Measurement data for the paper "From Empirical Measurements to Augmented Data Rates: A Machine Learning Approach for MCS Adaptation in Sidelink Communication"
[1] Rokoni, A. A., Schäufele, D., Kasparick, M. and Stańczak, S., 2023. From Empirical Measurements to Augmented Data Rates: A Machine Learning Approach for MCS Adaptation in Sidelink Communication. Submitted to VTC2023-Fall.
Data is saved as pandas dataframe and can be loaded using
df = pd.read_parquet('dataset_merged.parquet')
| Column | Unit | Notes |
|---|---|---|
| new_time_epoch | s | from GPS |
| latitude_user1 | ° | from GPS |
| longitude_user1 | ° | from GPS |
| speed_user1 | km/h | from GPS |
| latitude_user2 | ° | from GPS |
| longitude_user2 | ° | from GPS |
| speed_user2 | km/h | from GPS |
| distance | m | from GPS |
| SNR | dB | from UE |
| RSRP | dBm | from UE |
| RSSI | dBm | from UE |
| NOISE POWER | dBm | from UE |
| RX_GAIN | dBm | from UE |
| Rx_Power | dBm | from UE |
| MCS | from UE | |
| round |