=Paper=
{{Paper
|id=Vol-2332/paper-07-012
|storemode=property
|title=
Analyzing the Impact of ITS Mobile Node Antenna HPBW on Primary Network SINR
|pdfUrl=https://ceur-ws.org/Vol-2332/paper-07-012.pdf
|volume=Vol-2332
|authors=Anna Shchesniak,Roman Kovalchukov,Aleksandr Ometov
}}
==
Analyzing the Impact of ITS Mobile Node Antenna HPBW on Primary Network SINR
==
https://ceur-ws.org/Vol-2332/paper-07-012.pdf
60
UDC 004.72
Analyzing the Impact of ITS Mobile Node Antenna HPBW on
Primary Network SINR
Anna Shchesniak* , Roman Kovalchukov† , Aleksandr Ometov‡
*
Department of Wireless Telecommunications,
ITMO University,
Birzhevaya Liniya 14, St.-Petersburg, 199034, Russia
†
Department of Applied Probability and Informatics,
Peoples’ Friendship University of Russia (RUDN University),
Miklukho-Maklaya str. 6, Moscow, 117198, Russia
‡
Lab. of Electronics and Communications Engineering,
Tampere University of Technology,
Korkeakoulunkatu 10, 33720, Tampere, Finland
Email: aleksandr.ometov@tut.fi
The development of communication systems worldwide provides an additional load on both
licensed and unlicensed spectrum. One of the biggest segments influencing the unlicensed
one is Intelligent Transportation Systems (ITS) as part of the Smart City paradigm. One of
the potential solutions to reduce the interference picture is by improving the spatial reuse
of the system, i.e., by utilizing directional antennas on the vehicle side. This work aims to
analyze the radiation pattern spatial characteristics of the antenna installed on the vehicle
to be developed for cases when static ITS infrastructure nodes are located on the roadside
light poles and primary network operating in the same frequency range is located in different
locations: same light pole; roadside unit; or building. As a result, the recommendations
regarding the antenna parameters are given for each case.
Key words and phrases: V2I, ITS, antenna analysis, HPBW, SINR.
Copyright © 2018 for the individual papers by the papers’ authors. Use permitted under the CC-BY license —
https://creativecommons.org/licenses/by/4.0/. This volume is published and copyrighted by its editors.
In: K. E. Samouylov, L. A. Sevastianov, D. S. Kulyabov (eds.): Selected Papers of the 12th International Workshop on
Applied Problems in Theory of Probabilities and Mathematical Statistics (Summer Session) in the framework of the
Conference “Information and Telecommunication Technologies and Mathematical Modeling of High-Tech Systems”,
Lisbon, Portugal, October 22–27, 2018, published at http://ceur-ws.org
Shchesniak A., Kovalchukov R., Ometov A. 61
1. Introduction and background
Today, the number of the Internet of Things (IoT) nodes is growing tremendously [1].
One of the most significant IoT market niches is indeed related to vehicular communica-
tions [2]. The connectivity opportunities between mobile nodes regarding standardization
have already taken shape, and the first deployments would face the market soon forming
the paradigm of Intelligent Transportation System (ITS) [3, 4].
The communications between vehicles in ITS are commonly classified into two
big groups: vehicle-to-vehicle (V2V) [5] and vehicle-to-infrastructure (V2I) commu-
nications [6]. Some researchers combine them into Vehicle-to-Everything giant [7, 8].
Conventionally, the wireless links in both cases were utilizing omnidirectional antennas
for the data exchange. However, this approach may remarkably influence already de-
ployed networks operating within the same frequency spectrum and, thus, new solutions
should be developed aiming at decreasing the signal-to-noise ration on the primary
(already deployed or prioritized) networks.
One of the examples to be utilized is the implementation of smart antenna arrays
allowing to enable additional spatial reuse by producing narrow radiation pattern main
beam and nulls in interference directions and the possibility of diversity on the receiving
and transmitting side [9]. The other option is to utilize antenna steering solutions [10,11]
that require more space concerning deployment but are generally cheaper to develop.
The use of any of the solutions provides higher throughput, better reliability, and lower
interference. This work provides a vision of how the spatial antenna characteristics allow
reducing the signal to interference plus noise ratio of the primary network while the ITS
radio network is considered as secondary one.
Antenna arrays allow to control their radiation patterns and specify the characteristics
by selecting the phase and amplitude excitations at the antenna elements [12]. Scanning
arrays, in which the maximum of the radiation pattern can be oriented at different points
in space, are based on controlling the phase excitation between the antenna elements.
The proper amplitude-phase distribution of the individual antenna elements makes it
possible to form the required radiation pattern by controlling the main characteristics of
the antenna array, such as the half power beamwidth (HPBW), beamforming direction,
sile lobe level (SLL), etc.
Adaptive antenna arrays are a separate class of devices [13]. Due to the availability
of an adaptive processor, such antenna systems can dynamically adapt to changes in the
surrounding signal and interference environment, forming nulls in interference directions
and radiation maximum in the target signal arrival direction. In this work, authors do
not utilize systems with adaptive processors because due to initial conditionals all target
and interference signal directions of arrival are known in before so that no complex
algorithms are needed. Moreover, according to previously developed analytical model [14]
there was an assumption that at all points of the mobile node antenna are oriented
with radiation maximum pointing towards static nodes of the secondary network while
moving the radiation pattern.
The rest of the paper is organized as follows. The description of the ITS antenna
solutions is given in Section 2. The system model is given in Section 3. Numerical
results are provided in Section 4. The last section concludes the paper.
2. Directed antenna solutions for ITS
The architecture of antenna solutions with dynamic directivity control of the main
beam was discussed in many works [9, 15] as a promising solution concerning economical
expenditures in comparison with full adaptation systems. The beam switching technology
is more straightforward to implement since the beamforming arrangement can be designed
by applying matrix adders – the Butler matri [16] and the Blass matrix [17]. These
matrices are multipoles, and their inputs are connected to the outputs of the antenna
array individual elements, and the outputs correspond to specific beams. Such matrices
consist of directional couplers and phase shifters.
62 APTP+MS’2018
Another solution is to use sector antenna arrays with the fixed shape radiation
pattern [18]. In this case, the main beam of each antenna array covers a specific sector
of angles. These solutions could be used on the stationary unit side. Such configuration
may lead to the intersection in the space of the beams of contiguous antenna arrays.
Thus, the target signal could be received by a number of directional antenna arrays
but with different strength. The most straightforward algorithm for determining the
target signal angle of arrival is based on the signal strength analysis, thus choosing the
beam (and therefore the angle sector) where the signal strength has its maximum. Such
a system requires a switching mechanism that processes the connection of each sector
antenna array with a standalone receiver.
One more approach is to use electronic beam scanning in a passive antenna array
with electronic control of parasitic reactive elements. This solution allows to generate
the main beam in a given direction and to adapt to sources of interference with low
computational complexity. This solution consists of one active radiating element and
a number of passive parasitic elements located at a short distance from the central
active one and representing a reactive load. The angular direction of the beam depends
on the reactive impedance of the parasitic elements and can be determined using a
matching circuit based on an electronically controlled varactor diode. The advantage is
the absence of feeder paths to individual elements since the currents in the elements are
induced by electromagnetic coupling. The elements are located at small distances from
each other to ensure sufficient electromagnetic interaction, and such compactness makes
this solution suitable for placement on the roof of the vehicle.
The fourth solution is based on using one receiver and antenna array with digital
beamforming technique [19]. Traditional signal processing with digital beamforming
represents the simultaneous processing of the signal incident at individual antenna array
elements and, thus, requires that the number of receivers be equal to the number of
antenna elements. However, for compact and inexpensive solutions, the use of several
receivers is unjustified. One of the alternative methods is based on the local antenna
elements spatial multiplexing. This method corresponds to the sequential switching
on/off of individual antenna elements. The disadvantage of this system is the phase
shift caused by the time delay while switching between individual antenna elements.
These phase shifts can be compensated for, as the switching time is known.
Adaptive antenna systems with electronic or electro-mechanical beam scanning also
allow controlling the direction of the radiation maximum and the position of nulls.
Thus, such antennas can adapt to changing signal-to-interference conditions. Adaptive
antennas are more complicated to implement because they require an adaptive processor.
An example of a solution with partial adaptation is a phased array antenna (PAA)
with digital phase shifters [20]. The adaptation criterion is based on minimizing the
output power of the interfering signals. The brute force method is not practical for
the static nodes with a large number of elements and multi-bit phase shifters, but as
solutions for ITS, when the antenna array can be two to four elemental, and phase
shifters are controlled by several bits (have a coarse discretization), this technique is
entirely justified.
3. System model
The system model is shown in Fig. 1. Here, we consider the most straightforward
scenario when the vehicle is approaching the closest static receiver of the secondary
network 𝑅𝑥0 (transmitter’s beam is formed towards the corresponding receiver) and
produce interference to the primary static network 𝑅𝑥1 receiver. 𝑅𝑥1 is positioned
horizontally. The transmitter on the mobile node 𝑇 𝑥0 has a directive antenna with is
approximated as a die pyramid with the corresponding 𝛼𝑣 and 𝛼ℎ characteristics. The
height of the 𝑇 𝑥0 installation with respect to the ground level is ℎ1 .
Typically, 𝑅𝑥0 is located on the roadside infrastructure, i.e., light poles, public
transport stops, etc. Considering the metropolitan scenario, such installations happen
every 30–60 meters and the antenna placement height ℎ2 may vary. The height of 𝑅𝑥1
is equal to ℎ3 meters.
Shchesniak A., Kovalchukov R., Ometov A. 63
Interference
Figure 1. Scenario of interest
The distance between the nodes of the secondary network, mobile 𝑇 𝑥0 and static
𝑅𝑥0 , is represented by 𝑑1 and could vary from 100 to 0 meters, which represents the
mobile node movement. Value 𝑑2 describes the distance between 𝑅𝑥0 and 𝑅𝑥1 and is
set to 5 meters in this work. The distance 𝑑3 from the edge of the road to 𝑇 𝑥0 is 3
meters. Values 𝑑4 and 𝑑5 represent the distances from the edge of the road to 𝑅𝑥0 and
𝑅𝑥1 correspondingly.
For the channel path loss, we have utilized 3GPP 38.901 model for UMi (𝑃 𝐿1 , 𝑃 𝐿2 )
and InH if the distance between target equipement is less than 10 meters (𝑃 𝐿0 ) [21].
⎧
⎨𝑃 𝐿0 , if 1𝑚 ≤ (𝑑1 + 𝑑2 ) ≤ 10𝑚
⎪
⎪
𝑃 𝐿𝑈 𝑀 𝑖−𝐿𝑂𝑆 = ,
⎪𝑃 𝐿1 , if 10𝑚 ≤ (𝑑1 + 𝑑2 ) ≤ 𝑑𝐵𝑃
⎪
⎩
𝑃 𝐿2 , if 𝑑𝐵𝑃 ≤ (𝑑1 + 𝑑2 ) ≤ 5𝑘𝑚
where
𝑃 𝐿0 = 32.4 + 17.3𝑙𝑜𝑔10 (𝑑𝐼 ) + 20𝑙𝑜𝑔10 (𝑓𝑐 ),
𝑃 𝐿1 = 32.4 + 21𝑙𝑜𝑔10 (𝑑𝐼 ) + 20𝑙𝑜𝑔10 (𝑓𝑐 ),
𝑃 𝐿2 = 32.4 + 40𝑙𝑜𝑔10 (𝑑𝐼 ) + 20𝑙𝑜𝑔10 (𝑓𝑐 ) − 9.5𝑙𝑜𝑔10 (𝑑2𝐵𝑃 + (ℎ3 − ℎ1 )2 ),
and the threshold is calculated as
𝑓𝑐
𝑑𝐵𝑃 = 4(ℎ3 − ℎ𝑒 )(ℎ1 − ℎ𝑒 ) ,
𝑓
where 𝑓𝑐 – is the central carrier frequency, 𝑐 = 3 · 108 m/s – is the speed of light, the
effective height are calculated as ℎ3 and ℎ1 equal ℎ3 = ℎ3 − ℎ𝑒 meters, ℎ1 = ℎ1 − ℎ𝑒
meters, ℎ𝑒 meters – is the parameter related to the vehicle height. For UMi ℎ𝑒 is selected
as 1 meter according to the specification.
Let us further consider three scenarios of interest: (i) The first scenario represents
the case when 𝑅𝑥0 and 𝑅𝑥1 are located in the same physical location. Here 𝑑4 = 𝑑5
meters. (ii) In the second scenario, 𝑅𝑥0 is located at the light pole while 𝑅𝑥1 is moved
on the height of the 3rd floor of the nearby building. (ii) The third scenario corresponds
to situations when 𝑅𝑥0 is located at the light pole while 𝑅𝑥1 is on the roof of the public
transport stop (roadside unit).
64 APTP+MS’2018
Table 1
Main system parameters
Parameter Value
Frequency band 4.900 − 5.925 GHz
𝑅𝑥1 sensitivity −68 dBm
𝑅𝑥1 antenna gain 23 dBi
𝑅𝑥1 antenna HPBW 8𝑜 x 8𝑜
𝑇 𝑥0 Tx power 19 dBm
The primary network equipment is a wireless bridge Tsunami Quick Bridge 8200 that
allows providing high-speed backhaul access for the Internet providers [22]. The main
system parameters are given in Table 1.
4. Numerical results
The numerical evaluation was executed in the MatLab 2018a environment. The target
of interest in this paper is to compare the SINR on the primary network receiver side
with the allowable value based on the receiver sensitivity while the target modulation is
QAM-64 and the PER equal 10%. Thus, the reliable operational value of the primary
network is 27 dB. In order to evaluate the mobile node antenna, we change the HPBW
of the 𝑇 𝑥0 antenna from 10 to 40 degrees in both panes. The 𝑅𝑥0 antenna is supposed
to be a sector antenna covering the approaching vehicle side of the road.
The results of the first scenario are given in Fig. 2. Since in this work we only focus
on smart antenna beam control, we assume that scanning antenna array, which is based
on phase excitation at antenna elements, is used. Note, HPBW and SLL are changing
with different scan angles. In could be concluded that HPBW in elevation plane has
almost no influence on the 𝑅𝑥1 SINR. This is mainly due to the lack of height difference
between 𝑅𝑥0 and 𝑅𝑥1 . Here, for most of the vehicle position (45–100 meters from the
receiver), the SINR falls within acceptable bounds. While analyzing smaller distances,
it could be concluded that effective HPBW is azimuth plane should be smaller than 15𝑜 .
Lowering it also provides better results in a trade-off to the developed antenna cost due
to the need to increase the number of antenna elements.
Fig. 3 represents the second scenario. Similarly to the previous case, the effective
primary network operation is reached in some vehicle locations. In contrast, the regions
with acceptable SINR has slightly increased due to the better spatial separation of 𝑅𝑥0
and 𝑅𝑥1 . Note, the ineffective operation may be faced in close proximity between 𝑇 𝑥0
and 𝑅𝑥0 due to non-zero side-lobe interference.
Shchesniak A., Kovalchukov R., Ometov A. 65
The third scenario results are shown in Fig. 4. Here, 𝑅𝑥1 is located on the road-side
units. Here, the propagation characteristics follow the same pattern as in previous
scenarios.
5. Conclusions
Based on the obtained results, it could be concluded that for real-life ITS scenario
with vehicular node equipped with a smart antenna, the antenna HPBW in azimuth
plane should be not more than 15 degrees taking into account the aim to minimize
negative influence on the primary network. HPBW in elevation plane is not such a
critical parameter, however, the narrower the beam, the better the value of the SINR.
The authors are currently developing the smart antenna system prototype which
would fulfill the obtained in this paper requirements.
Acknowledgments
The work is partially supported by Doctoral training network in ELectronics, Telecom-
munications and Automation (DELTA).
Figure 2. Placement of 𝑅𝑥0 and 𝑅𝑥1 on the light pole
66 APTP+MS’2018
Figure 3. Placement of 𝑅𝑥0 on the light pole and 𝑅𝑥1 on the building
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