dichasus-cf1x-part1 Dataset: Distributed Antenna Setup in Industrial Environment, Day 2, First 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, 8 antennas (part of arrays B, C and D) are missing from the dataset.

Experiment Setup

The Arena 2036 factory floor. The measurement area is on the left at the very back of the hall. Source: ARENA2036 e.V.

Antenna arrays B (left) and D (right)

Picture taken from behind antenna array A

Picture taken from behind antenna array B

Picture taken from behind antenna array D

Ceiling structure of the factory hall

The measurement area is delineated by the orange traffic cones.

Top View: Left half of measurement area

Top View: Measurement area in factory environment, partly obstructed by the container.

Complete floorplan of the ARENA2036 research factory, with our L-shaped measurement area at the bottom left.

Detailed plan of the L-shaped measurement area, including antenna array names. Not to scale, only rough sketch!

MOBTX robot with tachymeter in the background.

Measurement area as seen from the position of the tachymeter.

One wing of the measurement area as seen from the position of the tachymeter.

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Data Analysis

Mean received signal-to-noise ratio by position

Mean received signal-to-noise ratio by position, seen from Antenna Array A

Mean received signal-to-noise ratio by position, seen from Antenna Array B

Mean received signal-to-noise ratio by position, seen from Antenna Array C

Mean received signal-to-noise ratio by position, seen from Antenna Array D

SNR by receive antenna index

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Antenna Configuration

Python: Import with TensorFlow

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

raw_dataset = tf.data.TFRecordDataset(["tfrecords/dichasus-cf12.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), (24))

	# 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), (24, 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), (24))

	# 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()

Reference Channel Compensation

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-part1,
	author    = {Euchner, Florian and Gauger, Marc},
	publisher = {DaRUS},
	title     = {{CSI Dataset dichasus-cf1x-part1: Distributed Antenna Setup in Industrial Environment, Day 2, First Part}},
	doi       = {doi:10.18419/darus-3150},
	url       = {https://doi.org/doi:10.18419/darus-3150},
	year      = {2022}
}

Download

This dataset consists of 1 file. Descriptions of this file as well as download links are provided below.

dichasus-cf12

Textual Description

The robot takes a similar circular trajectory multiple times, sometimes alternating between two paths.

5.4 GB

File Size

27510

Points

2185.8 s

Duration

Mean SNR by Position Plot

SNR by Antenna Boxplot

Channel Coefficient Vector Plot (Example)

View on DaRUS Download

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:

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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:

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