dichasus-0d5x Dataset: Distributed Arrays: Indoor LoS, Lab Room

Distributed measurement with two separate antenna arrays in an indoor lab room. Mostly line-of-sight dataset with vacuum robot-mounted transmitter.

50.000 MHz

Signal Bandwidth

1024

OFDM Subcarriers

162462

Data Points

9483.6 s

Total Duration

42.7 GB

Total Download Size

32

Number of Antennas

Indoor

Type of Environment

1.272000 GHz

Carrier Frequency

Distributed

Antenna Setup

2D LiDAR

Position-Tagged

Experiment Setup

Data Analysis

Warnings

Antenna Configuration

Antenna 1: Antenna array near wall

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 [0.04409614 2.75381747 1.41292018] and the antenna points in direction [-1.02372397 0.01639262 0].
Antenna Channel Assignments
31 29 0 13 1 12 3 7
30 26 21 25 22 15 24 8

Antenna 2: Antenna array near window

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 [-2.53918909 0.03996753 1.53192728] and the antenna points in direction [0.01639262 1.02372397 0].
Antenna Channel Assignments
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-0d51.tfrecords", "tfrecords/dichasus-0d52.tfrecords", "tfrecords/dichasus-0d53.tfrecords", "tfrecords/dichasus-0d54.tfrecords", "tfrecords/dichasus-0d55.tfrecords", "tfrecords/dichasus-0d56.tfrecords", "tfrecords/dichasus-0d57.tfrecords"])

feature_description = {
	"cfo": tf.io.FixedLenFeature([], tf.string, default_value = ''),
	"csi": tf.io.FixedLenFeature([], tf.string, default_value = ''),
	"gt-interp-age-lidar": tf.io.FixedLenFeature([], tf.float32, default_value = 0),
	"pos-lidar": tf.io.FixedLenFeature([], tf.string, default_value = ''),
	"rot-lidar": tf.io.FixedLenFeature([], tf.float32, default_value = 0),
	"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 LiDAR position. Indicates quality of linear interpolation.
	gt_interp_age_lidar = tf.ensure_shape(record["gt-interp-age-lidar"], ())

	# Position of transmitter determined by vacuum robot LiDAR, in meters (X / Y coordinates)
	pos_lidar = tf.ensure_shape(tf.io.parse_tensor(record["pos-lidar"], out_type = tf.float64), (2))

	# Rotation of robot relative to its initial parking position, in radians
	rot_lidar = tf.ensure_shape(record["rot-lidar"], ())

	# 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_lidar, pos_lidar, rot_lidar, 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-0d5x,
	author    = {Florian Euchner and Marc Gauger},
	publisher = {DaRUS},
	title     = {{CSI Dataset dichasus-0d5x: Distributed Arrays: Indoor LoS, Lab Room}},
	doi       = {doi:10.18419/darus-2218},
	url       = {https://doi.org/doi:10.18419/darus-2218},
	year      = {2021}
}

Download

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

dichasus-0d51
Textual Description

First complete run of vacuum robot. Gain is 4dB higher than in 0d53-0d57.

5.9 GB

File Size

22332

Points

1380.8 s

Duration

dichasus-0d52
Textual Description

Second complete run of vacuum robot. Gain is 4dB higher than in 0d53-0d57.

6.8 GB

File Size

26033

Points

1532.7 s

Duration

dichasus-0d53
Textual Description

Third complete run of vacuum robot. Gain is 4dB lower than in 0d51 and 0d52.

6.4 GB

File Size

24416

Points

1413.9 s

Duration

dichasus-0d54
Textual Description

Fourth complete run of vacuum robot. Gain is 4dB lower than in 0d51 and 0d52.

5.8 GB

File Size

21894

Points

1280.7 s

Duration

dichasus-0d55
Textual Description

Fifth complete run of vacuum robot. Gain is 4dB lower than in 0d51 and 0d52.

6.1 GB

File Size

23410

Points

1329.0 s

Duration

dichasus-0d56
Textual Description

Sixth complete run of vacuum robot. Gain is 4dB lower than in 0d51 and 0d52.

5.8 GB

File Size

22206

Points

1273.5 s

Duration

dichasus-0d57
Textual Description

Seventh complete run of vacuum robot. Gain is 4dB lower than in 0d51 and 0d52.

5.8 GB

File Size

22171

Points

1272.8 s

Duration