=Paper=
{{Paper
|id=Vol-3097/paper9
|storemode=property
|title=5G Deployment Strategies for High Positioning Accuracy in Indoor Environments
|pdfUrl=https://ceur-ws.org/Vol-3097/paper9.pdf
|volume=Vol-3097
|authors=Maria Posluk,Jesper Ahlander,Deep Shrestha,Sara Modarres Razavi,Gustav Lindmark,Fredrik Gunnarsson
|dblpUrl=https://dblp.org/rec/conf/ipin/PoslukASRLG21
}}
==5G Deployment Strategies for High Positioning Accuracy in Indoor Environments==
https://ceur-ws.org/Vol-3097/paper9.pdf
5G Deployment Strategies for High Positioning
Accuracy in Indoor Environments
Maria Posluk1 , Jesper Ahlander1 , Deep Shrestha2 , Sara Modarres Razavi2 , Gustav
Lindmark2 and Fredrik Gunnarsson2
1
Department of Electrical Engineering, Linköping University, Linköping, Sweden
2
Ericsson Research, Linköping, Sweden
Abstract
Indoor positioning is currently recognized as one of the important features in emergency, commercial
and industrial applications. The 5G network enhances mobility, �exibility, reliability, and security to
new higher levels which greatly bene�t the IoT and industrial applications. Industrial IoT (IIoT) use-
cases are characterized by ambitious system requirements for positioning accuracy in many verticals.
For example, on the factory �oor, it is important to locate assets and moving objects such as forklifts.
The deployment design for di�erent IIoT environments has a signi�cant impact on the positioning per-
formance in terms of both accuracy and availability of the service. Indoor factory (InF) and indoor open
o�ce (IOO) are two available and standardized Third Generation Partnership Project (3GPP) scenarios
for evaluation of indoor channel models and positioning performance in IIoT use cases. This paper aims
to evaluate the positioning performance in terms of accuracy and availability while considering di�er-
ent deployment strategies. Our simulation-based evaluation shows that deployment plays a vital role
when it comes to achieving high accuracy positioning performance. It is for example favorable to deploy
the 5G Transmission and Reception Points (TRPs) on the walls of the factory halls than deploying them
attached to the ceiling.
Keywords
5G, indoor positioning, CRLB, GDOP, DL-TDOA, NLOS conditions, accuracy, availability, deployment
strategies
1. Introduction
Localization in cellular networks is primarily used to locate a user equipment (UE) in outdoor-
only scenarios, often by exploiting global navigation satellite system (GNSS) that can guarantee
meter-level accuracy. In the recent years, mainly since the third generation partnership project
(3GPP) Release 13 [7], the focus on indoor positioning has gained a lot of attention mainly due
the updated federal communications commission (FCC) requirements concerning emergency
services for indoor calls [4] and also to address many commercial use-cases that bene�t from
positioning information. Moreover, the presence of 5G which has the potential to improve the
indoor positioning estimations to sub-meter level accuracy [8] and provides an opportunity to
enable a plethora of applications in manufacturing industry.
IPIN 2021 WiP Proceedings, November 29 – December 2, 2021, Lloret de Mar, Spain
� mariaposluk@gmail.com ( Maria Posluk); jesper.ahlander@gmail.com ( Jesper Ahlander);
deep.shrestha@ericsson.com ( Deep Shrestha); sara.modarres.razavi@ericsson.com ( Sara Modarres Razavi);
gustav.lindmark@ericsson.com ( Gustav Lindmark); fredrik.gunnarsson@ericsson.com ( Fredrik Gunnarsson)
© 2020 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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In the 5G positioning study in 3GPP Release 16 [1], the objective was to achieve indoor posi-
tioning accuracy below 3m, however the supported 5G positioning features have the technology
that has potential to support much more precise positioning. The wide bandwidths, beam-based
systems, and higher numerology of 5G compared to 4G all enable improved positioning resolu-
tion. Moreover, dense and tailored deployments with small cells and large overlaps improve
accuracy and, together with beam-based transmissions, provide more spatial variations that
can be exploited for radio frequency �ngerprinting [5].
One important aspect in the outcome of any cellular positioning method is the deployment
of the nodes which perform the transmission and reception of the signals. It is well-known that
the number of nodes and how they are distributed within the serving area has a great impact
on the positioning accuracy. Due to this dependency, there is always a trade-o� between how
accurate the position estimation can be and how complex and costly the positioning deployment
is [9]. In 5G architecture the concept of cells are represented by transmission and reception
points (TRPs) that transmit reference signals during a positioning occasion to be used by the
UEs to perform measurements for UE localization. When it comes to indoor positioning the
TRP deployment becomes even more prominent because the indoor environment contains more
obstacles leading to higher non line of sight (NLOS) conditions that add challenges in achieving
a high positioning accuracy [6].
There is already an extensive research on the topic of indoor positioning exploiting dif-
ferent technologies. However, most of these studies aim to explore and study the accuracy
and the potential of each indoor positioning solution assuming a �xed deployment which is
typically optimized to provide decent communication services. Analyzing the impact of di�erent
deployment strategies on positioning performance therefore remains unexplored.
In this paper, our aim is to analyze di�erent aspects of TRP deployment strategy and un-
derstand its impact on 5G indoor positioning performance. The results are then analyzed for
the purpose of understanding how positioning accuracy and availability relates to di�erent
deployments. This paper serves as a comprehensive summary of a well-studied thesis work
[10].
To provide a good overview of the conducted study, this paper is organized in six sections.
Section 2.1 introduces downlink time di�erence of arrival (DL-TDOA) method which is the
considered positioning technique, as well as the two analysis methods Cramér-Rao lower Bbund
(CRLB) and geometric dilution of precision (GDOP). Section 3 provides introduction to the
indoor open o�ce (IOO) and indoor factory (InF) scenarios as de�ned by 3GPP. Moving on,
simulation results and performance evaluations are presented in sections 4 and 4.4 while Section
5 draws some conclusive remarks.
2. Methodology
In this section, we �rst de�ne the DL-TDOA method and further explain how we assess a
lower bound on the positioning accuracy. Finally, we explain how we evaluate the impact of
deployment geometry independent and decoupled from other positioning errors.
2.1. Downlink Time Di�erence of Arrival (DL-TDOA) based Positioning
The 5G DL-TDOA is a positioning method, in which the UE measures the downlink time of
arrival (TOA) of positioning reference signals (PRSs) from di�erent TRPs, and reports the time
di�erence of arrival measurements for these TRPs to the location server in relation to the timing
of a reference TRP. The positioning estimation is then done by performing multilateration based
on the UE reported timing measurements.
Once the timing measurements are obtained, they are translated to distance observations by
multiplying it with the speed of light. A general measurement equation at time t has the form,
yt = h(✓t ) + et , (1)
where yt is the measurement, h(✓t ) is a nonlinear measurement model and et is noise. All
mentioned variables are vectors. The variable ✓t = [xt yt zt ]T is the 3D position of the UE.
In general, the function h(✓t ) will implicitly depend on the known positions of the N TRPs,
pi = [xi y i z i ]T , i 2 {1, 2, . . . , N }.
A TOA observation based on TRP i 2 {1, 2, . . . , N } in an asynchronous network can be
expressed by,
i
yTOA = |✓ pi | + i + ei , (2)
where i is the unknown clock bias between the UE and TRP i [11]. If the TRPs are synchronized
with each other but not with the UE, then i = j 8i, j 2 {1, 2, . . . , N }. In this case a DL-TDOA
measurement is obtained by taking the di�erence between two TOA measurements,
j, i j i
yDL-TDOA = yTOA yTOA = |✓ pj | |✓ pi | + e j ei , (3)
where i denotes reference TRP and j denotes another TRP for which timing measurements are
performed by the UE.
2.2. Cramér-Rao Lower Bound (CRLB)
It is often useful to state a lower bound on the performance of any unbiased estimator. The
CRLB which expresses a lower bound on the variance of unbiased estimators of a deterministic
(�xed, though unknown) parameter, stating that the variance of any such estimator is at least as
high as the inverse of the Fisher information, serves that purpose. In order to compute the CRLB
one needs to �rst determine the Fisher information matrix (FIM), I(✓). When the measurement
noise is additive white Gaussian noise (AWGN), the FIM becomes [11],
I(✓) = H T (✓)R 1 H(✓), (4)
where H(✓) = r✓ h(✓) and R is the measurement noise covariance matrix. The information is
additive for independent observations [12]. CRLB is �nally given by
Cov(✓ˆ) I 1 (✓). (5)
In positioning studies, plotting the positioning error in meters is a relevant performance metric
and it is achieved by calculating the root mean square error (RMSE). A lower bound for the
RMSE of an estimator is obtained by taking the square root of the trace of CRLB:
p
RMSE = E [(x x̂)2 + (y ŷ)2 + (z ẑ)2 ]
p (6)
tr(I 1 (✓)).
When only the horizontal UE positioning error is of interest, then a lower bound can be obtained
from the FIM as
p
RMSE = E [(x x̂)2 + (y ŷ)2 ]
q (7)
I1, 11 (✓) + I2, 12 (✓)
with I1, 11 (✓) and I2, 12 (✓) being the diagonal elements of I 1 corresponding to the x and y
directions [11].
2.3. Geometric Dilution of Precision (GDOP)
We seek to decouple the e�ect of the TRP deployment geometry on the positioning error
from the e�ects of other measurement errors. For that we use the (GDOP), which intends to
state how errors in the measurement will a�ect the �nal state estimation [13]. GDOP can be
computed for each position in the deployment area and it depends on the location of the TRPs
and the positioning method which is being used, in our case it is DL-TDOA. For a UE placed at
✓t = [xt yt zt ]T , the positioning error is essentially the product between GDOP at ✓t and the
measurement error and can be expressed as
(position estimation error) = GDOP ⇥ (measurement error).
For more details regarding GDOP, see e.g. [14].
3. Studied Scenarios
In this study two representative indoor scenarios, IOO and InF, de�ned in 3GPP are considered
[2]. The scenario speci�c parameters are outlined in the sub-sections to follow.
3.1. Indoor Open O�ice (IOO)
The IOO scenario is de�ned as a deployment area of dimensions 120 m ⇥ 50 m ⇥ 3 m designed
to capture typical indoor environments such as shopping malls and o�ces. The TRPs in this
type of environment are typically mounted on the ceiling at a height of 3 m [3].
3.2. Indoor Factory (InF)
The InF scenario represents factory halls of varying sizes and clutter densities. The area is 120
m ⇥ 60 m with a ceiling height of 5–25 m where the TRPs can be mounted.
The InF scenario has �ve di�erent variants. Out of the �ve, two variants named indoor
factory-sparse high (InF-SH) and indoor factory-dense high (InF-DH) are chosen for this study
Table 1
Descriptions of InF-SH and InF-DH.
Scenario parameter InF-SH InF-DH
E�ective clutter height 0–10 m
Concrete or metal walls and ceiling with metal
External wall and ceiling type
coated windows.
Big machines composed of reg- Small to medium metallic ma-
ular metallic surfaces. For ex- chinery and objects with irreg-
ample: several mixed produc- ular structure. For example:
Clutter type
tion areas with open spaces and assembly and production lines
storage/commissioning areas. surrounded by mixed small-
sized machines.
Typical clutter size 10 m 2m
Clutter density < 40% 40%
as they depict realistic InF scenarios with sparse and dense clutter densities. The sparse clutter
option speci�es an industrial factory �oor whose < 40% of the area is covered by machines,
storage shelves, assembly lines, etc. Likewise the dense clutter option speci�es an industrial
factory �oor with 40% of the area covered by machines, storage shelves, assembly lines, etc.
Descriptions of both InF scenarios are presented in Table 1.
3.3. Deployment Strategies
3GPP has de�ned standard TRP deployments for both IOO and InF. The proposed deployments
are mainly motivated to meet the quality of service (QoS) to support communication require-
ments of IIoT use cases. In IOO scenario 12 TRPs are deployed in two rows and in InF scenario
18 TRPs are deployed in three rows. In both scenarios an inter site distance (ISD) of 20m is
maintained in both x and y axes [3]. Inspired by the 3GPP speci�cations, in this paper we de�ne
and analyze the following three deployment strategies:
• Standard deployment: The 3GPP standard deployment. For fair comparison between
the deployment strategies in IOO and InF a 12 TRP deployment is considered for both
scenarios. In the text to follow, standard deployment refers to standard 12 TRP deployment
for both scenarios.
• Edge deployment: All TRPs are placed around the edges of the deployment area. In
the text to follow, edge deployment refers to 12 TRP (at the edges) deployment for both
scenarios.
• Mixed deployment:A mix of the standard and edge deployment strategies.
The IOO scenario is analyzed with the deployment strategies illustrated in Figures 1(a)–(c)
and the InF scenario with the deployment strategies in Figures 1(a), (b) and (d). Notice that
slightly di�erent mixed deployments are used for IOO and InF (compare Figures 1(c) and (d)).
The triangles in the �gures represent the 5G TRPs and the red lines show the deployment
area boundaries. Apart from these, in Section 4.3 we analyze the e�ect of TRP densi�cation in
positioning accuracy in IOO scenario.
30 30
20 20
10 10
y [m]
y [m]
0 0
-10 -10
-20 -20
-30 -30
-60 -40 -20 0 20 40 60 -60 -40 -20 0 20 40 60
x [m] x [m]
(a) Standard deployment (IOO and InF). (b) Edge deployment (IOO and InF).
30
30
20
20
10
10
y [m]
y [m]
0 0
-10
-10
-20
-20
-30
-30
-60 -40 -20 0 20 40 60 -60 -40 -20 0 20 40 60
x [m] x [m]
(c) Mixed deployment for IOO. (d) Mixed deployment for InF.
Figure 1: Deployment design strategies.
4. Performance Evaluation
In this section we show simulation results taking into account realistic channel models that depict
typical propagation environments in IOO and InF scenarios. An internal Ericsson simulation
tool is used which applies the channel model and parameters agreed by 3GPP in [2, 3] to generate
DL-TDOA measurements of PRSs between TRPs and UEs as well as LOS information and more.
These measurements are then used for positioning estimation. The PRSs are OFDM modulated
with parameters are reported in Table 2
In Fig. 2(a) and 2(b), contour plots of the GDOP for the standard and edge deployments in IOO
Table 2
Simulation parameters common for IOO and InF.
Simulation parameter Value
Carrier frequency 2 GHz
Subcarrier spacing 30 kHz
No of subcarriers 4096
PRS bandwidth 100 MHz
25 5 25 5
20 4.5 20 4.5
15 15
4 4
10 10
3.5 3.5
5 5
3 3
0 0
2.5 2.5
-5 -5
2 2
-10 -10
1.5 1.5
-15 -15
-20 1 -20 1
-25 0.5 -25 0.5
-60 -40 -20 0 20 40 60 -60 -40 -20 0 20 40 60
(a) GDOP, standard deployment. (b) GDOP edge deployment.
(c) CRLB standard deployment. (d) CRLB edge deployment.
Figure 2: Contour plots showing the GDOP and CRLB in IOO with standard and edge deployments.
are presented. The GDOP is in general lower and more homogeneous in the edge deployment
in comparison to the GDOP when the deployment is based on standard layout. The standard
deployment procures worse GDOP at the corners and along the short sides of the deployment
area. The contour plots indicate that the edge deployment is more favorable in terms of receiving
high positioning accuracy compared to the standard deployment as the dilution of positioning
accuracy due to the geometry of the TRP deployment is lower.
Fig. 2(c) and 2(d) show the CRLB (Equation (7)) of the UE position estimates obtained with
DL-TDOA. What can be noted is that the CRLB is larger for the standard deployment than for the
edge deployment. There are also much larger variations in CRLB for the standard deployment
compared to the edge deployment. In the standard deployment, the CRLB range is from 0.49m
to 3.94m while in the edge deployment the range is from 0.47m to 0.95m. These observations
show that the measurement error is not the only factor that limits the achievable UE localization
accuracy. The e�ect of the geometry of the TRP deployment is also of paramount interest.
Figure 3: CDFs showing the positioning error in IOO.
4.1. Indoor Open O�ice
NLOS conditions between the TRP i 2 {1, . . . , N } and the UE adds a positive o�set to Equation
(2) since the signal has traveled longer than the euclidean distance |✓ pi |. The o�set can result
in a biased position estimate.
Fig. 3 visualizes the cumulative distribution functions (CDFs) of the positioning error for the
standard, edge and mixed deployments in IOO when either all measurements or measurements
from only LOS TRPs are used. In this �gure we can observe that the edge and the mixed
deployments yield similar positioning accuracy and the achievable accuracy is higher than when
the TRP geometry is based on standard layout. This result is in line with our theoretical analysis
based on GDOP and CRLB. Moreover, the results indicate only small di�erences in positioning
accuracy between either using measurements from all TRPs or using the measurements only
corresponding to the TRPs that are in LOS condition with the UE. This can be understood by
considering the LOS statistics reported in Fig. 4. The histograms show the distribution of the
number of LOS TRPs in standard and edge deployment scenarios. It is worth noting that in both
scenarios there are very few UEs with less than four LOS links, which is the smallest number
of links required to obtain a position estimate using DL-TDOA, and therefore the positioning
error distribution looks similar when the UE position is estimated using measurements from all
TRPs and using measurements from only LOS TRPs. Moreover, more UEs can maintain a LOS
condition when the TRP deployment follows edge layout.
250 250
200 200
Number of UEs
150 Number of UEs 150
100 100
50 50
0 0
2 3 4 5 6 7 8 9 10 11 12 13 0 2 4 6 8 10 12
Number of LOS links Number of LOS links
(a) Standard deployment. (b) Edge deployment.
Figure 4: Histograms showing the number of UEs with a certain number of LOS links to the deployed
TRPs for the standard and edge deployments in IOO.
4.2. Indoor Factory
Fig. 5 shows the CDFs of the positioning error for the standard, edge and mixed deployments in
the InF-SH scenario. It can be observed that in general the achievable positioning accuracy is
higher than in IOO scenario and the edge deployment gives better accuracy than the standard
layout deployment of the TRPs. The higher accuracy positioning in InF-SH, in comparison
to the IOO, is due to higher probability of TRPs being in LOS with the UE. Fig.6a and Fig.6b
show the LOS statistics for InF-SH standard and InF-SH edge deployments. The LOS statistics
for InF-DH standard and InF-DH edge deployments are reported in Fig. 6c and Fig.6d. With
majority of UEs having no LOS link with any of the deployed TRPs InF-DH, where more than
40% of the area is covered by clutters, is a challenging environment for precise UE positioning.
In the sub-section to follow we address the problem of enhancing positioning accuracy in InF-
DH scenario by densi�cation of TRP. Moreover, the e�ect of TRP densi�cation on achievable
positioning accuracy in IOO scenario is also evaluated.
Figure 5: CDFs showing the positioning error in InF-SH.
4.3. TRP Densification
The e�ect of TRP densi�cation on achievable positioning accuracy in IOO scenario (both
standard and edge) is shown in Fig.7. It has been observed that the positioning accuracy
improves when the number of TRP is increased. When the number of TRP, following the
standard deployment layout, is increased beyond 36 the achievable positioning accuracy tends to
saturate. Similar observation can also be made when the number of TRP is increased following
the edge deployment layout. These observations validate that in either of the deployment
strategies the improvement in positioning accuracy is limited by the number of TRP that can be
deployed in a scenario like IOO.
Densi�cation of a given deployment can alternatively be done by successive addition of
TRPs in areas where the accuracy is bad. Fig. 8(a) shows the location of UEs with the largest
positioning errors in InF-DH scenario when the deployment is based on mixed layout. It has
been observed that the successive addition of TRPs helps improving the achievable positioning
accuracy. In particular the 90% positioning accuracy improves from 13.11m to 12.13m when
one additional TRP is deployed in the area where the positioning accuracy is bad. Deploying
two more TRPs to the problematic area helps improving the positioning accuracy from 13.11m
to 11.80m. Furthermore, the achievable positioning accuracy is 11.55m when three additional
TRPs are deployed in the problematic area as shown in Fig. 8(b)–(d). The CDF curves showing
the positioning error in the InF-DH scenario when the deployment strategy follows a mixed
layout is shown in Fig. 9 where it can be observed that the overall positioning accuracy only
improves slightly when TRPs are added.
200
180
160
140
Number of UEs
120
100
80
60
40
20
0
0 2 4 6 8 10
Number of LOS links
(a) Standard deployment in InF-SH. (b) Edge deployment in InF-SH.
600 700
600
500
500
400
Number of UEs
Number of UEs
400
300
300
200
200
100
100
0 0
-0.5 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 -0.5 0 0.5 1 1.5 2 2.5 3 3.5
Number of LOS links Number of LOS links
(c) Standard deployment in InF-DH. (d) Edge deployment in InF-DH.
Figure 6: Histograms showing the number of UEs with a certain number of LOS links to the deployed
TRPs for the standard and edge deployments in InF-SH and InF-DH.
Table 3
Positioning percentile errors for IOO deployment strategies.
Positioning error [m]
IOO deployment 80% 90% 95%
Standard 1.02 1.78 2.58
Edge 0.61 0.79 1.06
Mixed 0.60 0.92 1.19
4.4. Evaluation summary
In this paper we presented our study on achievable positioning accuracy in IOO and variety of InF
scenarios where the focus is to verify the e�ect of deployment strategies and TRP densi�cation
on DL-TDOA positioning technique. In IOO scenario, we observed e�ects of various strength
3 1.1
1
2.5
0.9
0.8
Positioning error [m]
Positioning error [m]
2
0.7
80% 80%
1.5 90% 0.6 90%
95% 95%
0.5
1
0.4
0.3
0.5
0.2
0 0.1
20 40 60 80 100 120 140 160 180 200 15 20 25 30 35 40 45 50 55 60 65 70
Number of 5G TRPs Number of 5G TRPs
(a) Standard. (b) Edge.
Figure 7: Curves showing the change in positioning accuracy at di�erent percentiles when the number
of TRPs increases in standard and edge deployments in IOO.
Table 4
Positioning percentile errors for InF deployment strategies.
Positioning error [m]
InF-SH InF-DH
InF deployment 80% 90% 95% 80% 90% 95%
Standard 0.29 0.54 0.78 15.59 25.83 46.47
Edge 0.16 0.23 0.34 10.18 13.56 18.04
Mixed 0.21 0.31 0.44 9.77 13.11 16.48
coming from the number of TRPs in the deployment, geometry of the deployment, and LOS
conditions that a�ect the achievable positioning accuracy. The observations can be summarized
as:
• An increasing number of TRPs proves to always improve the positioning accuracy and
also the availability.
• The deployment geometry is of high importance and must be considered as one of the
important factors for high accuracy UE localization.
• Better LOS conditions are favorable from a positioning perspective and improve the
positioning accuracy.
The study of the two InF scenarios highlighted the di�culty in achieving high accuracy
and availability indoor positioning in a densely cluttered deployment area in comparison to a
sparsely cluttered deployment area. The vital factor was an immense lack of the number of LOS
links which proved that decent LOS conditions are necessary for accurate positioning. The most
prominent di�erence between IOO and InF is that the clutter in InF, especially InF-DH, leads to
30 30
20 20
10 10
y [m]
y [m]
0 0
-10 -10
-20 -20
-30 -30
-60 -40 -20 0 20 40 60 -60 -40 -20 0 20 40 60
x [m] x [m]
(a) Original mixed deployment with a positioning error (b) Mixed deployment with one additional TRP and a po-
of 13.11 m at the 90 percentile. sitioning error of 12.13 m at the 90 percentile.
30 30
20 20
10 10
y [m]
y [m]
0 0
-10 -10
-20 -20
-30 -30
-60 -40 -20 0 20 40 60 -60 -40 -20 0 20 40 60
x [m] x [m]
(c) Mixed deployment with two additional TRPs and a (d) Mixed deployment with three additional TRPs and a
positioning error of 11.80 m at the 90 percentile. positioning error of 11.55 m at the 90 percentile.
Figure 8: Plots showing the positions of the worst 10% of the UEs with respect to positioning error for
the mixed deployment and the modifications where 1–3 TRPs are added in InF-DH.
a great loss in LOS links. This makes the trade-o� between favorable geometry and optimizing
the LOS links more di�cult in InF-DH than in IOO, where the geometry has the biggest impact
on the results. Table 3 and Table 4 summarizes the achievable positioning accuracy given that
the deployment is adjusted based on studied strategies in the studied scenarios.
5. Conclusions
In this paper, we investigated di�erent aspects of deployment and its impact on 5G indoor
positioning. While the standard deployment designs proposed by 3GPP always considers the
TRPs to be in the ceiling distributed evenly inside the indoor hall, our studies show that for
improved positioning performance, it is more favorable to mount the TRPs evenly on the wall
Figure 9: Positioning error CDFs for the mixed InF-DH deployment when 1-3 TRPs are added.
at the edges of the deployment area. In a more reasonable approach, in which both positioning
and communication performance requirements are considered, the strategy would be to have
a mixed deployment in which most of the TRPs are on the walls and few distributed on the
ceiling if the area of deployment is speci�cally cluttered due to the presence of machines and
other objects in a factory �oor. A �nal interesting �nding is that if one has to choose between
two deployments, one promoting deployment geometry and the other promoting the number
of LOS links, the choice should fall on the �rst option.
Acknowledgments
This work has been supported in parts by Ericsson Research AB and by the European Union’s
Horizon 2020 research and innovation programme under grant agreement No.871249 (Research
and Innovation Action), LOCalization and analytics on-demand embedded in the 5G ecosystem
for Ubiquitous vertical applicationS (LOCUS).
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