=Paper=
{{Paper
|id=Vol-3719/125
|storemode=property
|title=Angle of Arrival Estimation Using SRS in 5G NR Uplink Scenarios
|pdfUrl=https://ceur-ws.org/Vol-3719/paper5.pdf
|volume=Vol-3719
|authors=Thodoris Spanos,Fran Fabra,José A. López-Salcedo,Gonzalo Seco-Granados,Nikos Kanistras,Ivan Lapin,Vassilis Paliouras
|dblpUrl=https://dblp.org/rec/conf/wiphal/SpanosFLSKLP24
}}
==Angle of Arrival Estimation Using SRS in 5G NR Uplink Scenarios==
https://ceur-ws.org/Vol-3719/paper5.pdf
Angle of Arrival Estimation Using SRS in 5G NR
Uplink Scenarios
Thodoris Spanos1,2 , Fran Fabra3 , José A. López-Salcedo3 , Gonzalo Seco-Granados3 ,
Nikos Kanistras2 , Ivan Lapin4 and Vassilis Paliouras1
1
Dept. of Electrical and Computer Engineering, University of Patras, Greece
2
Loctio, Greece
3
Dept. of Telecommunication and Systems Eng., Universitat Autònoma de Barcelona (UAB), Spain
4
Radio Navigation Systems and Techniques Section, European Space Agency, The Netherlands
Abstract
This paper presents a comprehensive exploration of Angle of Arrival (AoA) estimation techniques in 5G
environments, using the Sounding Reference Signal (SRS) in Uplink scenarios both in simulations and
with actual measurements. Leveraging 5G capabilities, we investigate AoA algorithms for single-base
station positioning. The study includes simulations and practical tests on a developed dedicated testbed
featuring a base station equipped with a three-element Uniform Linear Array (ULA), considering Line
of Sight conditions in an open environment. The testbed, employing Ettus E312 as the transmitter and
Ettus N310 as the receiver, details waveform structures and reception processes. Additionally, our study
examines the performance of Angle of Arrival (AoA) estimation algorithms, such as Multiple Signal
Classification (MUSIC), Estimation of Signal Parameters via Rotational Invariant Techniques (ESPRIT),
and Joint Angle and Delay Estimation (JADE) ESPRIT. A MATLAB ray tracing propagation model of the
environment where the measurements are conducted, has been constructed. Simulation results using this
model are presented, along with the actual measurements. The obtained results affirm the effectiveness
of our implementation.
1. Introduction
Positioning using a single node or station is an alternative way to determine the user position
relying on angular and distance measurements, most suitable for the environments with limited
availability of the Global Navigation Satellite System (GNSS), such as indoors or deep urban
canyons. With the emergence of the mm-wave frequency range 2 (FR2) signals and large
antenna arrays in 5th Generation (5G) systems, positioning using a single node has recently
attracted increased research interest. A Long Term Evolution (LTE) localization testbed based
on the Direction of Arrival-Time of Arrival (DoA-ToA) has been implemented by Blanco et al.
[1]. Sun et al. performed a study on 3D positioning, expanding the Multiple Signal Classification
WIPHAL 2024: Work-in-Progress in Hardware and Software for Location Computation, June 25-27, 2024, Antwerp,
Belgium
$ tspanos@ece.upatras.gr (T. Spanos); franciscojose.fabra@uab.cat (F. Fabra); jose.salcedo@uab.cat
(J. A. López-Salcedo); gonzalo.seco@uab.cat (G. Seco-Granados); nikos.kanistras@loctio.com (N. Kanistras);
ivan.lapin@esa.int (I. Lapin); paliuras@ece.upatras.gr (V. Paliouras)
0000-0002-0877-7063 (T. Spanos); 0000-0001-8100-1520 (F. Fabra); 0000-0002-5392-6005 (J. A. López-Salcedo);
0000-0003-2494-6872 (G. Seco-Granados); 0000-0002-7337-1107 (N. Kanistras); 0000-0002-1847-5499 (I. Lapin);
0000-0002-1414-7500 (V. Paliouras)
© 2024 Copyright for this paper by its authors.
Use permitted under the Creative Commons License Attribution 4.0 International (CC BY 4.0).
1
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Thodoris Spanos et al. CEUR Workshop Proceedings 1–14
(MUSIC) algorithm to compute three parameters (azimuth angle, elevation angle and delay)
and comparing the results with the Expectation-Maximization (EM) algorithm [2]. Li et al.
propose a joint Angle of Arrival (AoA) and Time of Flight (ToF) method with a single base
station, utilizing Channel State Information (CSI) [3]. The MUSIC algorithm on a 4-element
Uniform Linear Array (ULA) is implemented on Universal Software Radio Peripheral (USRP)
nodes using LabVIEW platform by Tugrel et al. in [4]. In the same sense, Rares et al. evaluated
MUSIC and Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT)
algorithms using National Instrument devices in [5].
This paper focuses on the detailed modeling, simulation of real-world conditions, and ex-
perimentation of AoA estimation algorithms using Software Defined Radios (SDRs) in Uplink
scenarios, utilizing the Sounding Reference Signal (SRS). The angular estimation techniques
studied herein are integrated into a positioning testbed featuring a single base station. We
present our comprehensive exploration of various AoA techniques in 5G through simulations,
which initially informed the preliminary design of our testbed. Subsequently, we executed
practical tests using real signals on the established testbed. The presented analysis, sheds light
on the state-of-the-art AoA estimation algorithms and their performance metrics. The inclusion
of real scenario results in conjunction with simulations has provided valuable insights. This
iterative approach not only strengthens the reliability of our findings but also positions our
testbed as a robust platform for assessing the practical performance of diverse 5G technologies.
The paper is organised as follows: Section 2 presents essential information about the trans-
mitted waveforms, the implemented channel for simulations and the utilized signal processing
algorithms and methods. Section 3 offers an overview of the testbed, outlining its key features,
detailing its components and providing a comprehensive understanding of its setup. Moving
forward, Section 4 provides a summary of the simulation outcomes and the results obtained
from field tests. Finally, Section 5 summarizes the paper, offering concluding remarks and
insights.
2. Methodology
2.1. Waveform Structure
As proposed by the 5G standard, the SRS is used for uplink positioning. The transmitted SRS
sequence is generated and mapped into the allocated subcarriers according to [6]. Table 1
describes the parameters for the numerous 5G NR waveform configurations supported by
the testbed. These configurations have been identified based on different deployment scenar-
ios (static, pedestrian, vehicular). For static and pedestrian use cases, a subcarrier spacing
Δ𝑓 =30 kHz is considered, which is well-suited for low mobility scenarios. For the vehicular
use case, the subcarrier spacing of Δ𝑓 =60 kHz offers more robustness to Doppler effect in high
mobility scenarios, such as in vehicular environments, and to avoid inter-carrier interference
(ICI). In the scope of this paper, only waveform configurations I, II, and III are analyzed.
In addition, the SRS spans 4 consecutive OFDM symbols, transmitted over the whole signal
bandwidth, periodically in every slot and mapped to the physical resources according to a
comb-like pattern every 𝐾𝑇 𝐶 =2 subcarriers, which provides the highest density of SRS pilots
in the frequency domain.
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Table 1
5G Waveform Configurations
Configuration Numerology Frequency Band Subcarrier Bandwidth
Spacing (kHz) (MHz)
I - Static, Pedestrian 𝜇=1 ISM 2.4 GHz 30 20
II - Static, Pedestrian 𝜇=1 ISM 2.4 GHz 30 50
III - Static, Pedestrian 𝜇=1 Licensed 3.5 GHz 30 20
IV - Static, Pedestrian 𝜇=1 Licensed 3.5 GHz 30 50
V - Vehicular 𝜇=2 Licensed 3.5 GHz 60 20
VI - Vehicular 𝜇=2 Licensed 3.5 GHz 60 50
VII - Vehicular 𝜇=2 ISM 5.8 GHz 60 20
VIII - Vehicular 𝜇=2 ISM 5.8 GHz 60 50
After the subcarrier allocation, the known signal is transformed in the time domain having
the form of
S(t) = [𝑠(𝑡0 ), 𝑠(𝑡1 ), . . . , 𝑠(𝑡𝑁FFT ×𝑁OFDMsymbols −1 )]. (1)
2.2. Channel Model
Simulations were carried out via the MATLAB [7] ray tracing propagation model [8], in the
field trials environment, in every frequency band that was intended to be employed (2.4 GHz,
3.5 GHz). The shooting and bouncing (SBR) method was used for the creation of the rays, with
a maximum of one bounce per ray.
The simulation environment shown in Fig. 1, reveals a clear field, with the only notable
exception being the presence of a nearby building. An extra ray is generated through reflection
off the ground. According to the model, the reflection from the nearby building is not significant
for distances less than 20 meters or greater than 45 meters.
2.3. Signal Processing Algorithms
Assuming a 𝑀 -element ULA, 𝑀 copies of the transmitted signal propagate through the channel
and are received, one per antenna element, having the form
𝑃
∑︁
𝑥(𝑡) = A(𝜃𝑝 )𝐷𝑝 (𝑡)𝑠(𝑡 − 𝜏𝑝 ) + 𝑛(𝑡), (2)
𝑝=1
where A(𝜃𝑝 ) is the steering vector of the 𝑝-th path,
⎡ ⎤
(︁ 1 )︁
−𝑗2𝜋𝑓𝑐 𝑑 sin 𝜃𝑝
⎢ exp
⎢ ⎥
𝑐 ⎥
A(𝜃𝑝 ) = ⎢ .. ⎥, (3)
⎢ ⎥
⎢
⎣ (︁ . )︁
⎥
⎦
−𝑗2𝜋𝑓𝑐 (𝑀 −1)𝑑 sin 𝜃𝑝
exp 𝑐
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Thodoris Spanos et al. CEUR Workshop Proceedings 1–14
Figure 1: Propagation environment and ray creation via MATLAB ray tracing model
where 𝑓𝑐 is the carrier frequency, 𝑑 is the antenna element spacing, 𝑐 is the speed of light, and
𝜃𝑝 is the azimuth angle of path 𝑝.
2.3.1. Timing Synchronization/Slot Detection
Auto-correlation and cross-correlation methods have been explored for the timing synchroniza-
tion of the signal, and the detection of the beginning of the slot. Since the received waveform is
known at the base station, a cross-correlation method is preferred as the waveform of reference
is stored/generated at the receiver side and is not subjected to noise. The offset of the received
waveform in samples, compared to the original one is computed as the index 𝑛* , where the
largest peak of the output of the cross-correlator 𝑐(𝑛) occurs,
𝑛* = argmax 𝑐(𝑛), (4)
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Thodoris Spanos et al. CEUR Workshop Proceedings 1–14
where
𝐿seq −1
∑︁
𝑐(𝑛) = 𝑠* (𝑖)𝑥(𝑛 + 𝑖), (5)
𝑖=1
𝐿seq denotes the length of the transmitted waveform in samples and 𝑠* denotes the conjugate
of 𝑠.
2.3.2. AoA Estimation Algorithms
Three conventional Angle-of-Arrival algorithms have been studied and implemented: MUSIC
[9], ESPRIT [10] and Joint Angle and Delay Estimation (JADE) ESPRIT [11].
MUSIC is a super-resolution direction-finding algorithm, based on the eigenvalue decomposi-
tion of the received signal covariance matrix. The received signal 𝑥(𝑡) is transformed in the
frequency domain via the Fast-Fourier Transform (FFT) operation, to obtain Y. Considering
that one subcarrier represents a single measurement, Y has dimensions 𝑁antennas × 𝑁subcarriers .
The covariance matrix of Y is
R = E[YYT ]. (6)
As per (6), the covariance matrix has dimensions 𝑀 ×𝑀 . This results in the MUSIC algorithm
being able to detect up to 𝑀 − 1 sources. The eigenvectors corresponding to the 𝐷 larger
eigenvalues of the covariance matrix span the signal subspace Us = [v1 , . . . , v𝐷 ], whereas the
remaining eigenvectors span the noise subspace Un = [u𝐷+1 , . . . , u𝑀 −𝐷 ], where 𝐷 denotes
the number of sources.
As the covariance matrix R is hermitian, all its eigenvectors are orthogonal to each other,
meaning that the signal subspace is orthogonal to the noise subspace. The degree of orthogo-
nality in the MUSIC algorithm is measured by
1
MUSICSpectrum = , (7)
AH U H
n Un A
where A is the steering vector of received signal.
ESPRIT divides the main element array into a set of subarrays. Assuming the subarrays A1
and A2 , it holds that
A2 = A1 Ξ, (8)
(︁ )︁
where Ξ is a diagonal matrix whose main diagonal entries are 𝜉𝑖 = exp −2𝑗𝜋𝑑𝜆 sin 𝜃𝑖 , where 𝑑
is the antenna element spacing, 𝜃𝑖 denotes the Angle-of-Arrival 𝜃 at each antenna element and
𝜆 denotes the wavelength. Matrix Ξ applies a rotation to the matrix A1 . Following (8), ESPRIT
exploits similar rotations in matrices formed by the eigenvectors of the covariance matrix of
the measured data.
After eigenvalue decomposition is performed and the signal subspace is separated from the
noise subspace in a similar manner to the MUSIC algorithm, a matrix S is formed,
S = Us (:, 1:𝐷), (9)
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Thodoris Spanos et al. CEUR Workshop Proceedings 1–14
where Us is the matrix containing the eigenvectors. Notation in (9) denotes that S comprises
the first 𝐷 columns of U.
There exists a matrix P that contains rotational information such that the first set of eigen-
vectors yield the second set
S2 = S1 P, (10)
which can be obtained via the Least Squares method, i.e.,
S*1 S2
P= . (11)
S*1 S1
Lastly, the angle 𝜃 can be estimated in closed form, as
𝜃 = arcsin(𝜅), (12)
𝜑𝑖
where 𝜅 = 2𝜋𝑑 and 𝜑𝑖 is the 𝑖-th phase angle of the total 𝐾 eigenvalues of P.
2D ESPRIT forms a Hankel matrix by stacking copies of CSI matrix H. Similarly to 1D ESPRIT,
the shift-invariant properties of the matrix are exposed. However, in this (︁
case, similar
)︁ to the
−2𝑗𝜋𝜏𝑖
matrix Ξ, a matrix Ψ is defined, whose main diagonal entries are 𝜓𝑖 = exp 𝐿 , where 𝐿
is the channel length measured in symbol periods. A data model given by
H = ABF (13)
is satisfied, where A is the Khatri-Rao product of the steering matrix with the delay matrix, B
denotes the path attenuation and F is the DFT matrix with a Vandermonde structure.
A set of selection matrices is also defined, in which 𝜉𝑖 and 𝜓𝑖 corresponding to the angles and
delays, respectively, are estimated. The factor F in (13) ensures that a pairing between the angles
and delays is satisfied. The correct pairing is carried out by a joint diagonalization procedure.
To reduce complexity, all the computations can be kept in the real domain as described in [12].
2.3.3. SINR Computation
A crucial metric in assessing performance is the Signal-to-Interference-plus-Noise Ratio (SINR)
computation. As previously articulated, our signal transmission employs a comb-like pattern
every 𝐾𝑇 𝐶 =2 subcarriers, wherein every alternate subcarrier remains unoccupied. Conse-
quently, we compute the power associated with these vacant subcarriers, constituting the noise
component. By subtracting this noise power from the total power of the utilized subcarriers,
we ascertain the signal power. Subsequently, the SINR for each time slot is computed as
Putilized subcarriers − Pempty subcarriers
(︂ )︂
SINR = 10 log10 . (14)
Pempty subcarriers
3. Testbed Description
This testbed employs the transmission of representative 5G waveforms through SDRs, with the
base station featuring a three-element ULA. A 5G uplink waveform containing a number of
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Thodoris Spanos et al. CEUR Workshop Proceedings 1–14
Figure 2: User and Base Station setup during field trials
known SRS sequences depending on the bandwidth is generated and transmitted by the user in
the desired frequency. The user is responsible for generating, mapping and transmitting the SRS
sequences while the receiver processes the received signal and performs timing synchronization
and AoA estimation.
3.1. Testbed Equipment
The testbed setup utilizes an Ettus E312 as the transmitter and an Ettus N310 as the receiver,
presented in Fig. 2. Although the N310 has four RX channels, only three are used for AoA esti-
mation. This decision is driven by the N310’s architecture, which includes two daughterboards,
each with a pair of RX channels. All four channels of the N310 are originally misaligned in
phase, necessitating a phase offset compensation procedure. At first, phase offset compensation
is performed independently for the channel pairs within the N310 by feeding a tone signal to
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Thodoris Spanos et al. CEUR Workshop Proceedings 1–14
Figure 3: User and Base Station setup during the calibration procedure
all four channels using a 1-4 splitter. The phase difference between the two channels of each
pair is then computed by cross-correlating the received signals. These computed values are
stored and applied during signal processing to correct phase offsets, ensuring phase alignment
within each pair of channels. Moreover, because the N310’s two daughterboards use different
Local Oscillators (LOs) for their respective RX channel pairs, the N310 cannot inherently align
these pairs as per [13]. This results in random phase variations between runs. To address these
phase offsets from different LO initializations, a real-time calibration process is introduced. For
this procedure, as the testbed normally operates, a common signal is injected into one channel
of each pair via a 1-2 splitter, allowing the differential phase due to the different LOs to be
measured, but limiting the available channels for AoA estimation to three. This inherent phase
difference is then compensated in real-time, ensuring overall phase alignment. The testbed setup
for the initial phase offset compensation procedure is depicted in Fig. 3. Furthermore, dedicated
software has been developed to control and manage the testbed during experimentation. This
software facilitates seamless coordination, ensuring the overall optimization of the experimental
setup.
3.2. Testbed Signal Processing
Given the potentially impractical size of the IQ samples file, both in terms of storage and
processing efficiency, a snapshot technique has been implemented. Recognizing the necessity
of obtaining one angular estimation per second, this method ensures that only a fraction
of milliseconds for each second of the captured signal is retained on the host PCs, thereby
significantly diminishing the overall file size. In the context of 5G numerologies 1 and 2, relevant
to our work, one slot corresponds to 0.5 ms and 0.25 ms, respectively. Consequently, capturing
1 ms of signal is considered sufficient in all scenarios, as it aligns with the presence of a whole
slot at all times.
The signal processing scheme for one angular measurement each second, is described in
Algorithm 1.
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Thodoris Spanos et al. CEUR Workshop Proceedings 1–14
Algorithm 1 AoA Estimation with Ettus N310
1: while remaining size of the IQ Samples is greater than or equal to the size of a snapshot do
2: Align the phase of the snapshot for the individual channels of the two pairs, using values
computed in offline calibration.
3: Initialize a pointer at the first IQ sample. Load IQ samples corresponding to one snapshot.
4: while remaining size of snapshot is greater than or equal to twice the size of a slot do
5: Load IQ samples equivalent to two slots.
6: Determine the start of the 5G slot by cross-correlating loaded IQ samples with the
known waveform using (4), (5).
7: Align the phase of the two channel pairs by computing the phase difference of the
common signal.
8: Transform the received signal in the frequency domain by removing the cyclic prefix
and performing FFT. Form a grid for each antenna, with size 𝑁OFDM Symbols Per Slot ×
𝑁Subcarriers .
9: Extract the first 4 OFDM symbols of the slot that contain the SRS pilots.
10: Estimate Signal-to-Interference-plus-Noise Ratio (SINR) as described in Section 2.3.3.
11: Perform AoA estimation as outlined in Section 2.3.2.
12: end while
13: Remove outliers that deviate more than three scaled Median Absolute Deviations (MAD)
from the median of the data.
14: Average the remaining SINR and angular estimations of the snapshot. Increment the
pointer by the number of IQ samples corresponding to one snapshot.
15: end while
4. Results and Discussion
4.1. Simulations
Prior to conducting field trials, we utilized the MATLAB ray tracing propagation model to
simulate the performance of the three mentioned algorithms in the designated field environment,
as described in Section 2.2. Simulations were performed in the dedicated frequency bands
(2.4 GHz, 3.5 GHz), for an AoA of 0°, using the corresponding 5G signals. Initial tests measuring
received power were undertaken, and Additive White Gaussian Noise (AWGN) was introduced
in the simulations to replicate real Signal to Noise Ratio (SNR) conditions. As the ray tracing tool
offers a deterministic approach regarding the propagation channel, Monte-Carlo simulations of
200 measurements per distance for the given SNR values. Furthermore, The simulation analysis
assumes perfect antenna calibration. In reality, this is not the case as antenna calibration errors
decrease the accuracy of the angular estimation.
The simulation results, illustrated in Figs. 4 and 5, indicate that, under conditions of short
distances (below 20 meters) with a clear Line of Sight (LOS) path and only ground reflections, the
algorithms exhibit more stable performance. This stability contrasts with distances involving
reflections from the nearby building, as elaborated in Section 2.2. All three algorithms exhibit
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Thodoris Spanos et al. CEUR Workshop Proceedings 1–14
0.8°
2D ESPRIT
MUSIC
ESPRIT
0.6°
RMSE (°)
0.4°
0.2°
0°
10 20 30 40 50
Distance (m)
Figure 4: RMSE in degrees for reception with three antennas, simulated with ray tracing model for the
field trials area, at 2.4 GHz, with LOS at 0°.
0.8°
2D ESPRIT
MUSIC
ESPRIT
0.6°
RMSE (°)
0.4°
0.2°
0°
10 20 30 40 50
Distance (m)
Figure 5: RMSE in degrees for reception with three antennas, simulated utilizing a ray tracing model
for the field trials area, at 3.5 GHz, with LOS at 0°.
similar performance across both frequency bands, with occasional spikes in effectiveness
observed in the presence of reflections.
4.2. Field Tests
Preliminary field trials were conducted on the University of Patras campus to validate the oper-
ational capabilities of the testbed and evaluate the efficacy of Super-Resolution AoA estimation
algorithms with 5G signals in real scenarios. The positioning of the transmitter (Ettus E312)
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Thodoris Spanos et al. CEUR Workshop Proceedings 1–14
Table 2
Tests Overview
Configuration Frequency Band Angle of Arrival Test Duration (s) Distance (m) Snapshot Length
I ISM 2.4 GHz 0° 60 15 3 ms
II ISM 2.4 GHz 0° 60 15 3 ms
III Licensed 3.5 GHz 0° 60 15 3 ms
III Licensed 3.5 GHz 5° 60 15 3 ms
III Licensed 3.5 GHz 10° 60 15 3 ms
III Licensed 3.5 GHz 15° 60 15 3 ms
III Licensed 3.5 GHz 20° 60 15 3 ms
III Licensed 3.5 GHz 25° 60 15 3 ms
III Licensed 3.5 GHz 45° 60 15 3 ms
I ISM 2.4 GHz 0° 60 50 3 ms
II ISM 2.4 GHz 0° 60 50 3 ms
and the receiver (Ettus N310) adhered to the parameters established in the simulations outlined
in Section 4.1. As detailed in the aforementioned section, simulations highlighted a significant
impact on algorithm performance due to a robust reflection from a nearby building.
As the scope of this work targeted an open-field setting, two series of tests were undertaken to
minimize the impact of multipath: one at a close proximity of 15 meters and another at a greater
distance of 50 meters. This first set of tests was conducted in both the Industrial Scientific and
Medical (ISM) 2.4 GHz band and the Licensed 3.5 GHz band, using configurations I, II and III from
Table 1. The second set of tests was conducted in the ISM 2.4 GHz band, using configurations I
and II from Table 1. Table 2 outlines the conducted tests. The angle of arrival for the 2.4 GHz
band tests was fixed at 0°, while tests for the 3.5 GHz band were performed across the range of 0°
to 25° with a step of 5°. An additional test was executed at 45°. In all tests, a snapshot length of
3 ms was selected. Since all three configurations (I, II, III) use numerology 𝜇 = 1, each snapshot
contains 5 slots, resulting in 5 AoA estimations per snapshot, and therefore per second.
The outcomes of the static tests at the 3.5 GHz band, considering various angles of arrival
at a distance of 15 meters over a duration of 60 seconds, are illustrated in Fig. 6. Evaluation
of the angle of arrival resolution algorithms consistently demonstrates similar performance
across all scenarios, affirming the results obtained from the simulations. Minimal fluctuations
are observed, with particular notability in the cases of the MUSIC and ESPRIT algorithms.
Likewise, Figs. 7 and 8 illustrate the results of static tests conducted at the ISM band in
2.4 GHz, where the angle of arrival was fixed at 0°, spanning distances of 15 meters and 50
meters respectively. Once again, the performance of the algorithms exhibits a notable similarity,
particularly when contrasted with the overall fluctuations observed in the measurements.
In evaluating the overall performance of the testbed, it is crucial to acknowledge the com-
plexity of precisely setting the desired angle of arrival. Despite using equipment to align the
transmitter with the receiver in terms of angles, height, and floor tilt, minor discrepancies may
arise due to potential human error. With that said, the obtained results closely align with the
desired outcome in the majority of cases. Across various scenarios, we observe an accuracy of
less than 2° of error, accompanied by consistent results throughout the entire test duration. It is
noteworthy that certain significant fluctuations observed in the 3.5 GHz band test, particularly
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Thodoris Spanos et al. CEUR Workshop Proceedings 1–14
2D ESPRIT
MUSIC
ESPRIT
45°
Angle (°)
25°
20°
15°
10°
5°
0°
10 20 30 40 50 60
Time (s)
Figure 6: Performance evaluation of super-resolution algorithms in Licensed 3.5 GHz band for static
tests for different Angles of Arrival at 15 m distance
3°
Configuration I - 2D ESPRIT
Configuration I - MUSIC
Configuration I - ESPRIT
Configuration II - 2D ESPRIT
Configuration II - MUSIC
Configuration II - ESPRIT
Angle (°)
2°
1°
10 20 30 40 50 60
Time (s)
Figure 7: Angle of Arrival Estimation at 15 m distance in ISM 2.4 GHz band, using Configurations I and
II, with LOS at 0°
at 0°, 10°, and 15° angle of arrival, can be attributed to small channel fluctuations and potential
imperfections in the equipment. Furthermore, this particular frequency band is susceptible to
large amounts of interference due to the utilization of the spectrum by the mobile providers.
In conclusion, while the discrepancy between the simulation results and actual measurements
may seem significant, it is crucial to differentiate the simulation environment and models from
real-world conditions. The obtained results, overcoming factors such as interference, antenna
array imperfections, and equipment limitations, when also combined with the real channel,
highlight the robust performance of the testbed.
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0°
Configuration I - 2D ESPRIT
Configuration I - MUSIC
Configuration I - ESPRIT
Configuration II - 2D ESPRIT
Configuration II - MUSIC
Configuration II - ESPRIT
Angle (°)
−1°
−2°
10 20 30 40 50 60
Time (s)
Figure 8: Angle of Arrival Estimation at 50 m distance in ISM 2.4 GHz band, using Configurations I and
II, with LOS at 0°
5. Conclusions
In conclusion, this study provides a comprehensive evaluation of super-resolution algorithms
in 5G uplink scenarios through a combination of simulations and real experiments, within the
context of developing a positioning testbed. Field trials were emulated through simulations
using MATLAB ray tracing propagation model with 5G SRS signals across various distances.
Real experiments utilized Ettus E312 as the user and Ettus N310 as the base station, equipped
with a three-element ULA. Calibration of N310 channels, compensating for phase offsets, was
performed before signal processing. A snapshot technique for signal reception was implemented
to reduce the size of received IQ sample files and processing speed. Static tests conducted at
2.4 GHz and 3.5 GHz bands demonstrated comparable performance among all evaluated AoA
algorithms. Despite the preliminary nature of these tests, our testbed exhibited commendable
performance, delivering stability and accuracy in its results.
In addition to the findings presented in this study, it is noteworthy that our testbed serves
as an ongoing platform for further investigations. The current work involves continuous
measurements and additional experiments, particularly expanding into the 5.8 GHz band,
leveraging the capabilities of the developed testbed. This sustained effort aims to enhance
our understanding of 5G positioning technologies in real-world scenarios, contributing to the
refinement and expansion of practical applications.
6. Acknowledgements
The undertaken efforts were conducted within the framework of the Single Node Positioning
Testbed (SINGPOS) project funded by the European Space Agency (ESA).
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