# dichasus-ca0x Dataset: Industrial Environment LoS Day 1

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

Signal Bandwidth

OFDM Subcarriers

Data Points

Total Duration

### 32

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 13 31 29 3 7 1 12 30 26 21 25 24 8 22 15 28 5 10 14 6 2 16 18 19 4 23 17 20 11 9 27

## Python: Import with TensorFlow

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

raw_dataset = tf.data.TFRecordDataset(["tfrecords/dichasus-ca01_part1.tfrecords", "tfrecords/dichasus-ca01_part2.tfrecords", "tfrecords/dichasus-ca01_part3.tfrecords", "tfrecords/dichasus-ca01_part4.tfrecords", "tfrecords/dichasus-ca02.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), (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 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. The offset are constant over the complete dataset.

## 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-ca0x,
author    = {Florian Euchner and Marc Gauger},
publisher = {DaRUS},
title     = {{CSI Dataset dichasus-ca0x: Industrial Environment LoS Day 1}},
doi       = {doi:10.18419/darus-2507},
url       = {https://doi.org/doi:10.18419/darus-2507},
year      = {2022}
}

This dataset consists of 5 files. Descriptions of these files as well as download links are provided below.

dichasus-ca01_part1
Textual Description

First part of the first robot round trip, with very slow robot movement speed and a pseudorandom trajectory inside the measurement area.

7.2 GB

File Size

27595

Points

1600.6 s

Duration

dichasus-ca01_part2
Textual Description

Second part of the first robot round trip, with very slow robot movement speed and a pseudorandom trajectory inside the measurement area.

7.5 GB

File Size

28457

Points

1485.7 s

Duration

dichasus-ca01_part3
Textual Description

Third part of the first robot round trip, with very slow robot movement speed and a pseudorandom trajectory inside the measurement area.

7.6 GB

File Size

29092

Points

1394.9 s

Duration

dichasus-ca01_part4
Textual Description

Fourth part of the first robot round trip, with very slow robot movement speed and a pseudorandom trajectory inside the measurement area.

8.3 GB

File Size

31557

Points

1367.5 s

Duration

dichasus-ca02
Textual Description

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

8.5 GB

File Size

32234

Points

1342.3 s

Duration