# dichasus-cf1x-part2 Dataset: Distributed Antenna Setup in Industrial Environment, Day 2, Second Part

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. Due to a technical problem, 11 antennas (part of arrays B, C and D) are missing from the dataset.

Signal Bandwidth

OFDM Subcarriers

Data Points

Total Duration

### 21

Number of Antennas

### Indoor

Type of Environment

### 1.272000 GHz

Carrier Frequency

Antenna Setup

Position-Tagged

## Antenna Configuration

#### Antenna 1: Antenna Array A

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 [2.6747 -13.8973 1.560075] and the antenna points in direction [-0.4499506 0.89246853 0.03231686].
Antenna Channel Assignments
 3 1 10 12 17 2 4 8

#### Antenna 2: Antenna Array B

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 [-11.250275 -9.689025 1.5336] and the antenna points in direction [0.79575318 0.60534027 0.01843999].
Antenna Channel Assignments
 14 9 19 13 16

#### Antenna 3: Antenna Array C

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 [-1.531375 -15.0595 1.5772] and the antenna points in direction [0.21108544 0.97732106 0.0169259].
Antenna Channel Assignments
 0 6 7 20 18

#### Antenna 4: Antenna Array D

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 [-12.684425 -4.483325 1.78985] and the antenna points in direction [0.99369031 -0.08476847 0.073443].
Antenna Channel Assignments
 5 15 11

## Python: Import with TensorFlow

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

raw_dataset = tf.data.TFRecordDataset(["tfrecords/dichasus-cf13.tfrecords", "tfrecords/dichasus-cf14.tfrecords", "tfrecords/dichasus-cf15.tfrecords", "tfrecords/dichasus-cf16.tfrecords", "tfrecords/dichasus-cf20.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), (21))

# 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 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()

## 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-cf1x-part2,
author    = {Florian Euchner and Marc Gauger},
publisher = {DaRUS},
title     = {{CSI Dataset dichasus-cf1x-part2: Distributed Antenna Setup in Industrial Environment, Day 2, Second Part}},
doi       = {doi:10.18419/darus-3151},
url       = {https://doi.org/doi:10.18419/darus-3151},
year      = {2022}
}

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

dichasus-cf13
Textual Description

Robot drives along L-shaped meanders from one end of the area to the other.

4.6 GB

File Size

26464

Points

1349.7 s

Duration

dichasus-cf14
Textual Description

Robot trajectory follows some more meanders, different types in different sub-areas.

6.1 GB

File Size

35640

Points

1803.9 s

Duration

dichasus-cf15
Textual Description

Robot trajectory traces several symbols: INUE letters, heart, star, spiral. Slow robot movement.

2.2 GB

File Size

12702

Points

630.1 s

Duration

dichasus-cf16
Textual Description

Robot first slowly drives around the measurement area, then trajectory becomes a zigzag pattern inside the measurement area.

9.2 GB

File Size

53087

Points

2633.7 s

Duration

dichasus-cf20
Textual Description

The transmitter is static somehwere in the middle of the measurement area, the environment is also static.

0.0 GB

File Size

198

Points

9.9 s

Duration