dichasus-cf0x
Dataset: Distributed Antenna Setup in Industrial Environment, Day 1
rev2
Distributed antenna setup with line-of-sight (LoS) and non-line-of-sight (NLoS) channels, measured in the Arena2036 research factory campus environment.
Four antenna arrays with 8 antennas each distributed at the corners of an L-shaped area with a scatterer in between.
50.056 MHz
Signal Bandwidth
1024
OFDM Subcarriers
178116
Data Points
9347.6 s
Total Duration
46.8 GB
Total Download Size
32
Number of Antennas
Indoor
Type of Environment
1.272000 GHz
Carrier Frequency
Distributed
Antenna Setup
3D Tachymeter
Position-Tagged
48.000 ms
Measurement Interval
Experiment Setup
Data Analysis
Antenna Configuration
Antenna 1: Antenna Array A
6 | 2 | 16 | 18 |
28 | 5 | 10 | 14 |
Antenna 2: Antenna Array B
24 | 8 | 22 | 15 |
30 | 26 | 21 | 25 |
Antenna 3: Antenna Array C
3 | 7 | 1 | 12 |
0 | 13 | 31 | 29 |
Antenna 4: Antenna Array D
20 | 11 | 9 | 27 |
19 | 4 | 23 | 17 |
Python: Import with TensorFlow
#!/usr/bin/env python3
import tensorflow as tf
raw_dataset = tf.data.TFRecordDataset(["tfrecords/dichasus-cf02.tfrecords", "tfrecords/dichasus-cf03.tfrecords", "tfrecords/dichasus-cf04.tfrecords", "tfrecords/dichasus-cf05.tfrecords", "tfrecords/dichasus-cf06.tfrecords", "tfrecords/dichasus-cf07.tfrecords"])
feature_description = {
"cfo": tf.io.FixedLenFeature([], tf.string, default_value = ''),
"csi": tf.io.FixedLenFeature([], tf.string, default_value = ''),
"gt-interp-age-tachy": tf.io.FixedLenFeature([], tf.float32, default_value = 0),
"pos-tachy": tf.io.FixedLenFeature([], tf.string, default_value = ''),
"snr": tf.io.FixedLenFeature([], tf.string, default_value = ''),
"time": tf.io.FixedLenFeature([], tf.float32, default_value = 0),
}
def record_parse_function(proto):
record = tf.io.parse_single_example(proto, feature_description)
# Measured carrier frequency offset between MOBTX and each receive antenna.
cfo = tf.ensure_shape(tf.io.parse_tensor(record["cfo"], out_type = tf.float32), (32))
# Channel coefficients for all antennas, over all subcarriers, real and imaginary parts
csi = tf.ensure_shape(tf.io.parse_tensor(record["csi"], out_type = tf.float32), (32, 1024, 2))
# Time in seconds to closest known tachymeter position. Indicates quality of linear interpolation.
gt_interp_age_tachy = tf.ensure_shape(record["gt-interp-age-tachy"], ())
# Position of transmitter determined by a tachymeter pointed at a prism mounted on top of the antenna, in meters (X / Y / Z coordinates)
pos_tachy = tf.ensure_shape(tf.io.parse_tensor(record["pos-tachy"], out_type = tf.float64), (3))
# Signal-to-Noise ratio estimates for all antennas
snr = tf.ensure_shape(tf.io.parse_tensor(record["snr"], out_type = tf.float32), (32))
# Timestamp since start of measurement campaign, in seconds
time = tf.ensure_shape(record["time"], ())
return cfo, csi, gt_interp_age_tachy, pos_tachy, snr, time
dataset = raw_dataset.map(record_parse_function, num_parallel_calls = tf.data.experimental.AUTOTUNE)
# Optional: Cache dataset in RAM for faster training
dataset = dataset.cache()
Reference Channel Compensation
For this dataset, we are able to provide estimated antenna-specific carrier phase and sampling time offsets. These offsets occur due to the fact that the reference transmitter channel is not perfectly frequency-flat. To learn more about why these offsets occur and about their compensation, visit our offset calibration tutorial on this topic. Note that the estimates provided here are "best-effort" calculations. The phase and time offsets between antennas in the same array are usually very accurate, but for antennas that are spaced far apart, the results may be less precise. For this dataset, the reference transmitter channel seems to be somewhat unstable, i.e., phase and time offsets fluctuate over time. Therefore, we provide a file containing our phase and time offset estimates for each individual file in the dataset. You can download these estimates from the list of files below.How to Cite
Please refer to the home page for information on how to cite any of our datasets in your research. For this dataset in particular, you may use the following BibTeX:
@data{dataset-dichasus-cf0x,
author = {Euchner, Florian and Gauger, Marc},
publisher = {DaRUS},
title = {{CSI Dataset dichasus-cf0x: Distributed Antenna Setup in Industrial Environment, Day 1}},
doi = {doi:10.18419/darus-2854},
url = {https://doi.org/doi:10.18419/darus-2854},
year = {2022}
}
Download
This dataset consists of 6 files. Descriptions of these files as well as download links are provided below.
dichasus-cf02
dichasus-cf03
dichasus-cf04
dichasus-cf05
dichasus-cf06
Derived Channel Statistics
Channel statistics such as delay spread, k-Factor and path loss exponent are a good way to characterize a wireless channel measurement and to parametrize a channel model. Using estimation algorithms contributed by Janina Sanzi, we automatically extract the following channel statistics from the measured datasets: