# 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).

Signal Bandwidth

OFDM Subcarriers

Data Points

Total Duration

### 24.3 GB

Total Download Size

### 21

Number of Antennas

### Indoor

Type of Environment

### 1.272000 GHz

Carrier Frequency

Antenna Setup

Position-Tagged

## 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:

@data{dataset-dichasus-cb0x,
author    = {Florian Euchner and Marc Gauger},
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}
}

## Download

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

dichasus-cb00
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

16635

Points

1295.3 s

Duration

dichasus-cb01
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

32978

Points

1477.4 s

Duration

dichasus-cb02
Textual Description

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

4.0 GB

File Size

23045

Points

1228.2 s

Duration

dichasus-cb03
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

30432

Points

1345.1 s

Duration

dichasus-cb04
Textual Description

The robot leaves the measurement area.

2.0 GB

File Size

11539

Points

583.2 s

Duration

dichasus-cb05
Textual Description

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

2.2 GB

File Size

12799

Points

633.0 s

Duration

dichasus-cb06
Textual Description

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

2.3 GB

File Size

13393

Points

622.0 s

Duration