dichasus-cb0x Dataset: Industrial Environment LoS Day 2

Line-of-Sight (LoS) area in the Arena2036 research factory campus environment.

Same as dichasus-ca0x, but measured on the second day with different reference transmitter channel and a different environment outside the measurement area (for example, a car body was moved to stand just outside of the measurement area).

50.000 MHz

Signal Bandwidth


OFDM Subcarriers


Data Points

7184.2 s

Total Duration

24.3 GB

Total Download Size


Number of Antennas


Type of Environment

1.272000 GHz

Carrier Frequency


Antenna Setup

3D Tachymeter


Experiment Setup

Data Analysis


Antenna Configuration

Antenna 1: Main Array

This array has a vertical spacing of 0.118m and a horizontal spacing of 0.118m. In the dataset's cartesian coordinate system, its center is located at [-2.6706 7.878075 1.457425] and the antenna points in direction [-0.86082768 -0.50889655 0].
Antenna Channel Assignments
0 10 19 2 4 1 9
20 16 15 5
18 7 11 13
3 14 12 8 6 17

Python: Import with TensorFlow

#!/usr/bin/env python3
import tensorflow as tf

raw_dataset = tf.data.TFRecordDataset(["tfrecords/dichasus-cb00.tfrecords", "tfrecords/dichasus-cb01.tfrecords", "tfrecords/dichasus-cb02.tfrecords", "tfrecords/dichasus-cb03.tfrecords", "tfrecords/dichasus-cb04.tfrecords", "tfrecords/dichasus-cb05.tfrecords", "tfrecords/dichasus-cb06.tfrecords"])

feature_description = {
	"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)

	# 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), (21, 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), (21))

	# Timestamp since start of measurement campaign, in seconds
	time = tf.ensure_shape(record["time"], ())

	return 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:

	author    = {Euchner, Florian and Gauger, Marc},
	publisher = {DaRUS},
	title     = {{CSI Dataset dichasus-cb0x: Industrial Environment LoS Day 2}},
	doi       = {doi:10.18419/darus-2604},
	url       = {https://doi.org/doi:10.18419/darus-2604},
	year      = {2022}


This dataset consists of 7 files. Descriptions of these files as well as download links are provided below.

Textual Description

First robot round trip, with fast robot movement speed and a pseudorandom trajectory inside the measurement area. Not continous since some datapoints failed to decode due to a configuration error.

2.9 GB

File Size



1295.3 s


Textual Description

Second, longer robot round trip, with fast robot movement speed and a pseudorandom trajectory inside the measurement area.

5.7 GB

File Size



1477.4 s


Textual Description

Third robot round trip, with fast robot movement speed and a pseudorandom trajectory inside the measurement area.

4.0 GB

File Size



1228.2 s


Textual Description

Fourth robot round trip, with fast robot movement speed the same trajectory as dichasus-cb02 inside the measurement area.

5.2 GB

File Size



1345.1 s


Textual Description

The robot leaves the measurement area.

2.0 GB

File Size



583.2 s


Textual Description

Meandering path (crosswise) through measurement area, fast driving speed.

2.2 GB

File Size



633.0 s


Textual Description

Meandering path (lengthwise) through measurement area, fast driving speed.

2.3 GB

File Size



622.0 s


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:

RMS Delay Spread

The delay spread of a wireless channel is inversely proportional to the channel's coherence bandwidth and indicates how "spread out" the lengths of the various multipath propagation paths are. For every datapoint, the delay spread can be characterized by its root mean square value and the resulting delay spreads can be plotted over the measurement area:

Rician K-Factor

The Rician K-factor is defined as the power ratio between dominant and diffuse component, usually expressed in decibels. We estimate the K-factor with a moment-method based on the distribution of of channel coefficient powers. The resulting K-Factors be plotted over the measurement area: