# High-Rate Uninterrupted Internet-of-Vehicle Communications in Highways:
Dynamic Blockage Avoidance and CSIT Acquisition

Hao Guo, , Behrooz Makki, , Mohamed-Slim Alouini, , and Tommy Svensson

###### Abstract

In future wireless networks, one of the use-cases of interest is Internet-of-
vehicles (IoV). Here, IoV refers to two different functionalities, namely,
serving the in-vehicle users and supporting the connected-vehicle
functionalities, where both can be well provided by the transceivers installed
on top of vehicles. Such dual functionality of on-vehicle transceivers,
however, implies strict rate and reliability requirements, for which one may
need to utilize large bandwidths/beamforming, acquire up-to-date channel state
information (CSI) and avoid blockages. In this article, we incorporate the
recently proposed concept of predictor antennas (PAs) into a large-scale
cooperative PA (LSCPA) setup where both temporal blockages and CSI out-dating
are avoided via base stations (BSs)/vehicles cooperation. Summarizing the
ongoing standardization progress enabling IoV communications, we present the
potentials and challenges of the LSCPA setup, and compare the effect of
cooperative and non-cooperative schemes on the performance of IoV links. As we
show, the BSs cooperation and blockage/CSI prediction can boost the
performance of IoV links remarkably.

##  I Introduction

Suppose that, sitting in an autonomous drive vehicle driving in a highway, you
are watching Game of Thrones, and your smart phone gets disconnected. In ten
years, this will bring huge dissatisfaction for the users, leading to poor
rating for the network provider. Particularly, in the 6G era, users in their
own cars expect the same quality-of-service (QoS) as they have at home. Thus,
cellular networks will face with high data rate/reliability demands driven by
in-vehicle users running, e.g., 4K/8K video streaming and augmented reality
online gaming applications. This is a part of the Internet-of-Vehicle (IoV)
concept, for which high-rate uninterrupted connections are required.

Such reliable mobile IoV links are used not only to serve the in-vehicle users
but also for connected-vehicle/autonomous drive use-cases relying on vehicle-
to-infrastructure (V2I), in general V2X (X: anything), communications.
Currently, connected automated drive is a large business market with more than
$130 billion revenue in 2019, and is expected to garner more than $500 billion
by 2026 [1]. Thanks to V2X communications, traffic information becomes more
precise, improving the traffic efficiency/safety and reducing CO2 emissions.
However, V2X links have strict requirements. For example, 99.99% reliability
is required for network-assisted pedestrian protection, and considerably
higher reliability is foreseen with Levels 4/5 of self-driving vehicles being
commercially available by 2030 [2].

Along with connected-vehicle related communications, the V2X antennas mounted
on top of the vehicles can be utilized as intermediate moving relays
connecting the base stations and the in-vehicle users. This is motivated by
the fact that 1) compared to typical user equipments, such on-vehicle
transceivers may be equipped with more antennas/capable of advanced signal
processing techniques. Also, 2) compared to direct BS to in-vehicle links, the
MR implementation eliminates the vehicle penetration loss, which is measured
to be around 25 dB at 2.4 GHz [3] and even higher losses are foreseeable at
higher frequencies. Such dual functionality of IoV transceivers, however,
increases the rate/reliability requirements of the BS-OVT links even further.

To increase the data rate, different methods are considered, among which
network densification and millimeter wave (mmw) communications are the
dominant ones. Network densification refers to the deployment of multiple BSs
of different types providing more resource blocks per area. Particularly, it
is expected that soon small cells will be densely deployed to assist the
existing macro BSs. On the other hand, mmw transmission offers wide bands and
simplifies multi-antenna communications. However, it has inherent physical
issues limiting its availability: Along with sensitivity to atmospheric
variations, mmw transmission experiences poor propagation characteristics and
has high free-space path loss. For these reasons, it is foreseen that mmw-
enabled small cells will be deployed at low heights, e.g., on lamp post, with
a short coverage range. With a low antenna height, however, the probability of
blockage increases. This is important because the mmw signals suffer from high
penetration loss/low diffraction from objects and are significantly
deteriorated by blockage. Therefore, to guarantee uninterrupted high-rate
communications, it is preferred to avoid blockage.

With stationary/low mobility networks, along with deployment optimization, one
can well _learn_ the network deployment and avoid (semi-)static blockages
(such as buildings and trees) via resource association [4], cooperative (CP)
transmission [5] or the incorporation of relays [6]/intelligent reflecting
surfaces [7]. Alternatively, back-up non-line-of-sight (NLoS) links can be
found during the initial beam training phase and, if a line-of-sight (LoS)
link is blocked, the connection can switch to the back-up link(s) [8].

The problem, however, becomes more challenging in moving, e.g., V2I, networks
with high-speed vehicles driving in highways. First, deployment optimization
does not help in moving networks. Second, the back-up NLoS solution is not of
interest because, along with signal attenuation, 1) good reflectors (building,
etc.) are rare in highways, and 2) compared to urban scenario with rich
scattering, such back-up links sustain for a shorter period [8]. Third,
considering low-height small cells in highways, the blockers are mainly busses
and trucks passing by the cars. Thus, probabilistic blockage prediction or
(machine learning-based) deployment learning methods may not cope well with
the network dynamics at high speeds/frequencies. This is specially because not
only the blockage results in excessive signal-to-interference-plus-noise ratio
(SINR) degradation, but also the system performance is considerably affected
by the channel aging phenomena where with high speeds the channel state
information at the transmitter (CSIT) soon becomes inaccurate.

In this article, we investigate different methods of dynamic blockage
avoidance and CSIT prediction in IoV networks. We concentrate on a highway
scenario where OVTs, either used for connected-vehicle applications or as an
MR serving the in-vehicle users, receive high-rate uninterrupted streams
transmitted by CP BSs. For that, we incorporate the recently proposed
predictor antenna (PA) concept [9, 3] into a large-scale cooperative PA
(LSCPA) setup with CP communications among BSs, and utilize the information
provided by different vehicles to avoid not only temporal blockages but also
the CSIT out-dating. While concentrating on mmw CP IoV communications, we
investigate the potentials of back-up sub-6GHz techniques, as robust solutions
in the cases with high vehicle speeds/CSIT accuracy requirements, as well as
multiple-input multiple-output (MIMO) communications. As we show, joint
dynamic blockage and CSIT prediction improves the throughput and reliability
of IoV links by orders of magnitude.

##  II MMW IoV Communications in Highways

Vehicle connectivity enables different services such as see-through, vehicle
platooning and automated overtake. This, however, requires continuous
information exchange between the vehicles and the infrastructure.
Communications between neighbouring mobile units, normally carried out via
sidehaul links, is out of our scope, and we concentrate on V2I communications.
Here, initial intelligent transportation systems were based on dedicated
short-range communication (DSRC) standards, e.g., IEEE 802.11p/DSRC,
supporting data rate in the order of 10 Mbps. On the other hand, connected
vehicles are currently equipped with hundreds of sensors, including ultra-
sound proximity sensors, cameras and light-detection-and-ranging (LiDAR)
system, where, for instance, LiDAR system alone may require data rates up to
100 Mbps for blind spot removal [10]. Obviously, DSRC-based systems are not
enough for such applications. Also, although Long-Term Evolution-Advanced
(LTE-A) based solutions, as suggested by, e.g., [11], boosts the rates to up
to 100 Mbps, we still need higher rates. This is particularly because, with
the IoV concept, the OVTs not only provide connected-vehicle communications
but also work as MRs between the BSs and in-vehicle UEs.

With this background and motivated by the 5G progress in mmw communications,
multi-antenna mmw transmission is a powerful candidate for IoV providing
massive bandwidth and high-gain beamforming (BF). Mmw systems are typically
considered in static networks, e.g., wireless backhaul [6]. With mobility,
however, the availability of the mmw-based narrow BF systems is prone to
different challenges including CSIT inaccuracy, beam misalignment and
blockage. Particularly, with a blockage the path loss exponent increases from
$\sim 2.8$ in the cases with LoS connections to $\sim 3.9$ in NLoS
communication, and outdated CSIT/out-of-beam signal reception lead to
significant SINR drop.

The key features of highway environments, however, help to solve the dynamic
blockage/channel aging issues to some extent; In a highway, connected vehicles
are likely to drive along the same set of lanes with controlled speeds. Here,
an example is vehicle platooning setup, i.e., a group of vehicles traveling
closely together with speed/direction controlled by the lead vehicle.
Moreover, as illustrated in Fig. 1, in a highway, slow large vehicles (e.g.,
buses, trucks) travel typically in the outermost lanes, while high-speed
vehicles drive along the innermost lanes. Then, temporal blockage occurs if a
large vehicle drives between a user and its serving BS located on lamp posts
along the highway. In this case, as we explain in the following, one can use
channel prediction and utilize CP communication/information exchange between
the vehicles and BSs not only to avoid channel aging but also the temporal
blockages.

Few works study mmw communication in highways. Particularly, the interesting
work of [10] develops theoretical models to describe the downlink (DL) V2I
network in the presence of blockage and perfect CSIT. Also, [12] designs a
spatial model for mmw V2X networks with platooning. Finally, [13] develops a
relay-based V2V CP network assuming perfect CSIT.

Figure 1: Illustration of the proposed LSCPA system. The information provided
by a vehicle in the front ($\text{UE}_{1}$) at time $T_{1}$ is used to predict
both blockage and small-scale fading for data transmission to the following
vehicles ($\text{UE}_{2}$) at time $T_{n}$.

##  III Large-scale Cooperative Predictor Antenna

One option for CSIT prediction at high speeds is to use Kalman-filter based
channel estimation, while the prediction performance is decent only within
0.1-0.3 times the wavelength [3]. Also, other alternative techniques such as
prediction of the environment channel and CSIT adaptation based on the UEs
location information may be inaccurate and come with high information exchange
overhead (see [3] for a review of different small-scale CSIT prediction
schemes at high speeds).

Using the PA concept for small-scale CSIT acquisition in MR systems has been
recently studied in, e.g., [3, 9]. Here, the idea is to deploy a PA at the
front of a vehicle which is used to estimate the channel quality that is
observed by receive antennas, aligned behind the PA, when they reach the same
point as the PA (See Fig. 1). In this way, the PA intelligently transforms the
delay between estimating the channel and utilizing it into spatial separation
between the PA and the RA. As shown in [3, 9], the single-antenna PA setup can
boost the prediction horizon of the state-of-the-art Kalman prediction-based
schemes with 0.1-0.3 wavelengths prediction horizon to up to three times the
wavelengths at 2.68 GHz and a velocity of 45-50 km/h. Also, the relative
performance gain of the PA system increases in the cases with massive MIMO
communications [3, 9].

In practise, the feasibility of the PA concept in a single vehicle has been
verified by testbed-based experiments. For example, in 2014, Dresden, Germany,
we showed that the cross-correlation between the PA and the RA maintains high
(more than 97%) for up to three times the wavelength [14]. Also, in 2018, [9]
performed channel measurements in Stuttgart, Germany, where a 2.18 GHz system
was set up with 64 antennas at the BS and 2 monopole antennas at the vehicle.
Assisted by the PA, such a massive MIMO DL system could reach the ideal BF
gain even in NLoS scenarios. Interestingly, [9] showed that, assisted by the
PA, applying zero-forcing transmission at the BS to two spatially multiplexed
cars leads to 20 to 30 dB SINR gain.

The current PA setups are designed for 1) communication between a single
vehicle and a BS, 2) non-cooperative (NCP) communications, and 3) predicting
the small-scale fading. Here, we develop the LSCPA concept for both blockage
and channel aging avoidance in a CP fashion as described in the following (see
Fig. 1).

With LSCPA, each vehicle is equipped with, possibly, one set of PA/RA antennas
on top of the vehicle. While the PA of each vehicle can work as a small-scale
predictor for its own RA, as in the existing PA setups [3, 9], the antennas on
top of the front vehicle can enable reliable high-rate data transmission to
the vehicles behind from different perspectives:

  * •

If the front vehicle, i.e., $\text{UE}_{1}$ in Fig. 1, detects a blockage,
e.g., by a truck passing by, it estimates the speed of the blocker and
$\text{UE}_{2}$. Then, $\text{UE}_{1}$ informs one (or, multiple) BS(s) about
both the instantaneous CSIT of the location as well as the speed of the
blocker and $\text{UE}_{2}$. Knowing the speeds, the BS(s) can predict the
slots, e.g., Slot $T_{n}$ in Fig. 1, when the second vehicle will be blocked
by the truck and, in those slots, using CP BSs, we may switch to a different
BS to serve the second vehicle blockage-free.

  * •

Without blockage, the instantaneous CSIT provided by the PA of the front
vehicle can assist the data transmission to the second vehicle. On one hand,
utilizing the CSIT of the front vehicle, both antennas of the second vehicle
are used for data reception when the second vehicle reaches the same position
as where the antenna of the first vehicle was sending pilots. On the other
hand, the PA of the second vehicle can also be used for CSIT acquisition and
such an information is combined with that provided by the first vehicle to
improve the CSIT accuracy. Here, one can consider different methods to combine
the CSIT provided by different antennas.

In this way, not only the small-scale fading prediction can be improved, but
also the temporal blockages are detected and avoided. The former can be
utilized to compensate for the channel aging effect, while the latter enables
dynamic blockage avoidance by enabling a vehicle to switch to another BS when
predicted that a possible blockage may occur to the link from the vehicle to
its currently allocated BS. Also, our setup may simplify hybrid automatic
repeat request (HARQ)-based retransmissions and reduce the number of
retransmissions/ end-to-end (E2E) delay. This is intuitively because when
blockage is avoided, with high probability, large signal-to-noise ratio (SNR)
drops are not experienced. Thus, even if a signal is not correctly decoded, it
needs a small boost in the SNR which can be provided by, e.g., a single
retransmission. Alternatively, one can rely only on diversity and use Type I
HARQ with low implementation complexity. Finally, the blockage prediction may
provide seamless handover without service interruption, because the BSs may
have $n\geq 1$ slots gap for handover (see Fig. 1).

Note: We concentrate on the highway scenario where dynamic blockage is
probable. Here, LSCPA setup is applicable as long as the vehicles are on the
same lane, in a car platooning setup or with known moving speeds. However, the
same approach is applicable in, e.g., high-speed trains and trams. With
trams/trains, the moving trajectory is more predicable, which improves the
positioning accuracy and simplifies communication, specially at high
frequencies.

Figure 2: E2E throughput of the second vehicle vs transmit SNR, 32 antennas at
the BS, carrier frequency 28 GHz, speed 50 km/h, BS processing delay 5 ms,
codeword length 8000 symbols, antenna separation 6.6 times the wavelength and
$33\%$ blockage probability.

Considering the setup of Fig. 1 with two vehicles, two BSs and mmw spectrum,
Figs. 2-3 show the E2E throughput and the outage probability of the second
vehicle in the cases with 33% blockage probability. Here, considering unit-
variance noise power, transmit SNR is defined as the transmit power of the BS
in the log-domain, and the details of the parameter settings are given in the
figures captions.

E2E throughput is defined as the average number of correctly decoded
information bits per the E2E delay. The E2E delay is given by the transmission
delay plus the possible processing delay at the BS. Also, to evaluate the
effect of MIMO communications, considering 32 antennas at the BS, we present
the results for both cases with (one RA, one PA) and (two RAs, two PAs) setups
at the UEs. The results are presented for spatially-correlated Rayleigh fading
conditions using the Jake’s correlation model. In Figs. 2-3, we study the case
that, while the information provided by the front vehicle is used for blockage
avoidance, the small-scale CSIT is obtained from the PA(s) in self vehicle.
The potential of using the small-scale CSIT from the front vehicle is studied
in Fig. 6.

Figure 3: Outage probability versus transmit SNR, 32 antennas at the BS,
$33\%$ blockage probability, carrier frequency 28 GHz, speed 50 km/h, BS
processing delay 5 ms (affecting the cases with spatial mismatch), codeword
length 8000 symbols, and antenna separation 6.6 times the wavelength. The
outage threshold, i.e., the minimum data rate required by the UEs, is 0.3 bit-
per-symbol.

One of the main challenges of PA system is spatial mismatch, i.e., when the RA
does not reach the same position where the PA (either on the same or a
different vehicle) estimated the channel several time slots before. This type
of spatial mismatch affects the accuracy of CSIT and, consequently, the system
performance. For this reason, Figs. 2-3 present the results for both cases
with and without spatial mismatch. In the cases with no spatial mismatch, one
may consider an adaptive-delay transmission scheme where the transmission
delay can be dynamically adapted, as a function of the speed/antennas
distance, such that the BS sends the data to the RA at exactly the same
position as the PA performing CSIT acquisition process. Here, CSIT is perfect,
at the cost of extra transmission delay. Note that the adaptive-delay scheme
is applicable only for a range of speeds limited by the BS’s minimum required
processing delay (see Fig. 5). With a non-adaptive delay method, on the other
hand, one can always consider the BS’s minimum processing delay. This setup,
which is more suitable for slotted communications, is at the cost of possible
spatial mismatch/CSIT inaccuracy which can be modeled as a function of the
speed/spatial mismatch [3].

The following transmission techniques are compared in Figs. 2-3:

  * •

Method 1: CP-MIMO-perfect CSIT. With MIMO setup and maximum ratio transmission
(MRT) BF, two PAs and two RAs, i.e., a total of four antennas, are deployed on
the top of each vehicle, and, using the information provided by the front
vehicle, CP scheme is used to avoid blockages. Here, we consider perfect CSIT
at the BSs, i.e., adaptive-delay scheme, at the cost of extra transmission
delay. Finally, the blockage is avoided by switching to the closest non-
blocked BS.

  * •

Method 2: CP-MISO-perfect CSIT. Here, we consider the same setup as in Method
1, except that each vehicle is equipped with only two antennas; one RA and one
PA, i.e., the BS-RA link is MISO (S: Single).

  * •

Method 3: CP-MISO-mismatched CSIT. Considering CP BSs to avoid blockage, a
non-adaptive delay method is considered, based on the BS minimum processing
delay, at the cost of imperfect small-scale CSIT.

  * •

Method 4: NCP-MISO-perfect CSIT. Here, we study the case with NCP transmission
from a single BS at the cost of possible blockages. However, in each vehicle,
small-scale fading is predicted using the self PA.

  * •

Method 5: CP-MIMO-no CSIT. With a total of two antennas on top of each
vehicle, we deploy CP setup to avoid blockage. However, no small-scale CSIT is
considered and random BF is performed while both antennas at the vehicle are
used for data reception. This is a benchmark showing the gain of small-scale
CSIT acquisition.

  * •

Method 6: CP-MISO-no CSIT. The setup is similar to Method 5 with only one
antenna at the vehicle.

  * •

Method 7: NCP-MISO-no CSIT. The worst-case benchmark with no CP
framework/CSIT, and random BF.

Figure 4: An example of the sum throughput of $\text{UE}_{1}$ and
$\text{UE}_{2}$ in different time slots, 32 antennas at the BSs, carrier
frequency 28 GHz, transmit SNR 50 dB, channel bandwidth 50 MHz with noise
figure -174 dBm/Hz and antenna separation 6.6 times the wavelength.

According to Figs. 2-3, blockage predication and avoidance via BSs cooperation
leads to considerable E2E throughput and outage probability improvement. For
instance, considering the cases with single PA and RA on each vehicle and 50
dB transmit SNR, blockage avoidance results in $20\%$ throughput increment,
compared to the cases with NCP transmission (Fig. 2). Also, with an outage
probability $10^{-4}$ and the parameter settings of Fig. 3, the proposed LSCPA
scheme gives 20 dB SNR gain, compared the NCP scheme experiencing possible
blockages, even in the presence of perfect small-scale CSIT. Finally, using PA
to predict small-scale CSIT could reach a 120% gain in E2E throughput at 55 dB
transmit SNR as well as 18 dB SNR gain with an outage probability $10^{-4}$.

In summary, blockage (resp. small-scale CSIT) prediction is the main
performance booster in terms of outage probability (resp. throughput). In this
way, the proposed LSCPA method, with both blockage and CSIT prediction can be
an enabler for uninterrupted high-rate IoV communications. Finally, although
increasing the number of antennas at the vehicles improves the achievable
rate/reliability, for a broad range of parameter settings, the relative gain
of MIMO communication is marginal in the cases with perfect CSIT. With no
CSIT, however, MIMO setup leads to considerable outage probability/throughput
improvement (Figs. 2-3).

To demonstrate the LSCPA procedure in detail, considering an
$\text{UE}_{0}-\text{UE}_{1}-\text{UE}_{2}$ car platooning setup, Fig. 4
presents the sum throughput of $\text{UE}_{1}$ and $\text{UE}_{2}$ following
$\text{UE}_{0}$, possibly used as a predictor, within seven time slots. We
present the result for four cases, namely, 1) LSCPA using self PA for CSIT, 2)
non-LSCPA using self PA for small-scale CSIT acquisition, 3) LSCPA but no
PA/small-scale CSIT and random BF, and 4) no blockage avoidance/CSIT. As seen
in Fig. 4, the blockage deteriorates the system performance, during
$T_{2}-T_{3}$ and $T_{5}-T_{6}$ where $\text{UE}_{1}$ and $\text{UE}_{2}$ are
blocked by a truck, respectively, resulting in almost $40\%$ throughput drop
with small-scale CSIT prediction. For both cases with and without blockage
detection, the lack of small-scale CSIT leads to significant throughput
reduction, for instance, $67\%$ throughput drop with blockage avoidance.
However, deploying LSCPA the system experiences an almost constant QoS and the
throughput is improved, compared to the considered benchmark schemes.

Figure 5: E2E throughput in bit-per-second (bps) as a function of the speed.
We consider both 2.8 and 28 GHz frequencies and 32 antennas at the BS. We set
the transmit power to 50 dBm, noise spectrum density as -174 dbm/Hz, and
channel bandwidth for mmw (resp. sub-6GHz) as 80 (resp. 70) MHz, codeword
length 10000 symbols, antenna separation for mmw and sub-6 GHz are 6.6 and 0.6
times the wavelength, respectively.

The performance of the LSCPA method depends on the considered carrier
frequency and delay adaptation capability. For this reason, Fig. 5 studies the
effect of spatial mismatch, considering both 2.8 and 28 GHz without blockage.
Here, the E2E throughput is demonstrated for both adaptive- and non-adaptive-
delay methods.

Compared to sub-6GHz, the throughput is more sensitive to spatial mismatch and
speed variation at mmw spectrum (Fig. 5). On the other hand, adaptive-delay
method shows robust performance in various carrier frequencies. However, the
feasibility of adaptive-delay scheme is limited within a speed range, e.g.,
less than 50 km/h in Fig. 5, determined by the BS minimum required processing
delay. Importantly, there is not a single method providing the maximum
throughput; at low speeds, utilizing adaptive-delay method at mmw spectrum
leads to maximum throughput. At moderate speeds, however, the highest
throughput is achieved by exploiting spatial correlation and using non-
adaptive-delay scheme at 28 GHz. Finally, at high speeds, where the
sensitivity to spatial/BF mismatch increases, the maximum throughput is
achieved via non-adaptive-delay method operating at sub-6GHz.

Note that, although adaptive-delay method at mmw gives the highest throughput
for a range of speeds, from a network perspective, it may not be of interest
as it may introduce unplanned interference to the network. Moreover, in
practice cellular networks have a limited transmission time interval
granularity which may limit the efficiency of the adaptive-delay scheme.

Figure 6: E2E throughput as a function of the gap between the front and behind
vehicles. Carrier frequencies are set to 6 and 28 GHz. Numbers of antennas at
the BS are 64 for mmw and 4 for sub-6 GHz. The channel bandwidth is set to 10
MHz for both cases, vehicle speed 80 km/h, delay at the BS 5 ms, transmit
power 40 dBm, and the codeword length 8000 symbols.

Whether to use the self or the front vehicle PA for small-scale prediction
depends on, e.g., the carrier frequency, speed and number of antennas. Figure
6 elaborates on this point where, considering both mmw and sub-6GHz bands and
two antennas per vehicle, we study the throughput versus the gap between the
vehicles for the following alternative schemes:

  * •

One PA, one RA, self-vehicle PA: As in typical PA scheme [3], the front
antenna is used only for channel estimation required for data transmission to
the behind RA.

  * •

Two RAs, front vehicle PA: Here, the BS obtains the small-scale CSIT from the
front vehicle. Thus, using adaptive-delay scheme, both antennas of behind
vehicle are utilized for data reception while the transmission parameters are
adapted based on CSIT achieved through front vehicle with a delay penalty.

As seen, at mmw band the highest throughput is achieved by utilizing the self-
vehicle PA, unless for the cases with small gap between the vehicles where
exploiting the front vehicle CSIT and using both antennas of the behind
vehicle for data reception improves the throughput. At sub-6GHz, however,
exploiting the CSIT provided by the front vehicle is useful for a broad range
of gaps between the vehicles. In summary, there is a trade-off between
utilizing both antennas for data reception and the extra delay for data
transmission due to utilizing the front vehicle CSIT, and the maximum
throughput is achieved in a modular setup where the front vehicle (resp. the
self-vehicle PA) CSIT is used in the cases with small (resp. large) gap
between the vehicles. In practice, handling the spatial mismatch by utilizing
the CSIT from the front vehicle requires good alignment along the moving
direction. This can be achieved by, for example, coordinated control over
platooning setups, or different coaches in trains/trams.

##  IV Standardization Progress for IoV

Since Rel. 16, 3GPP is deeply involved in standardizations enabling IoV. To
complement LTE V2X, 3GPP Rel. 16 developed a new V2X standard based on 5G new
radio (NR) air interface. Here, different levels of automation levels ranging
from no automation (level 0) to full automation (level 5) and various car
platooning, advanced driving, extended sensors and remote driving use cases
are defined, where the QoS requirements increase with the automation level
(see [15] for details). Particularly, with advanced driving and extended
sensor use-cases, the vehicles share the data obtained from their own sensors
with the surrounding vehicles/infrastructure, to improve the perception of the
environment beyond that obtained by vehicles’ own sensors. Also, to better
serve the mobile nodes, dual connection-based mobility management and UE-based
handover (specially, dual active protocol stack (DAPS)-based handover), are
defined.

From relaying perspective, 3GPP Rel. 16-17 have specified standardizations for
integrated access and backhaul (IAB) as the main relaying method in 5G NR [6].
Here, although mobile IAB are yet not considered, a large part of the
specifications are compatible with mobility. Also, the backhaul adaptation
protocol introduced specifically for IAB enables routing between multiple
hops. Moreover, it is not unlikely that the introduction of mobile IAB,
mounted on top of vehicles, will be considered for future releases. In that
case, different issues such as movement of mobile IAB between different
central units, dynamic adaptation of routing tables, interference management,
etc., need to be handled. With both vehicle-to-anything (V2X) and MR
functionalities, the sensitivity of the mmw narrow BF to inaccurate CSIT/BF
mismatch and blockage should be carefully taken care of, where, along with
other methods, the LSCPA concept may be an attractive candidate.

##  V Conclusions

Aiming for uninterrupted IoV communications, we demonstrated the potentials of
utilizing the front vehicles information for dynamic blockage and small-scale
fading prediction in high-speed links. Introducing the ongoing standardization
attempts enabling IoV communications, we verified the effect of MIMO
transmission and different carrier frequencies on the network performance,
where the best E2E throughput is obtained in a modular setup of different
carrier frequencies/data transmission techniques, depending on the vehicles
distances/speeds. Our simulations show that the proposed LSCPA concept is a
potential solution to support future high-speed IoV links. However, there is
still room for theoretical/testbed evaluations identifying the potentials and
challenges of LSCPA.

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|  Hao Guo [S’17]<EMAIL_ADDRESS>is currently pursuing his PhD degree
with Department of Electrical Engineering, Chalmers, Sweden.
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|  Behrooz Makki [M’19, SM’19] works as Senior Researcher in Ericsson
Research, Sweden.
---|---
|  Mohamed-Slim Alouini [S’94, M’98, SM’03, F’09] is a Professor of Electrical
Engineering in King Abdullah University of Science and Technology, Saudi
Arabia.
---|---
|  Tommy Svensson [S’98, M’03, SM’10] is Full Professor in Communication
Systems, Chalmers, Sweden.
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  *[QoS]: quality-of-service
  *[IoV]: Internet-of-Vehicle
  *[V2I]: vehicle-to-infrastructure
  *[BS]: base station
  *[MR]: moving relay
  *[OVT]: on-vehicle transceiver
  *[mmw]: millimeter wave
  *[BSs]: base station
  *[CP]: cooperative
  *[NLoS]: non-line-of-sight
  *[LoS]: line-of-sight
  *[SINR]: signal-to-interference-plus-noise ratio
  *[CSIT]: channel state information at the transmitter
  *[OVTs]: on-vehicle transceiver
  *[PA]: predictor antenna
  *[LSCPA]: large-scale cooperative PA
  *[MIMO]: multiple-input multiple-output
  *[DSRC]: dedicated short-range communication
  *[LiDAR]: light-detection-and-ranging
  *[LTE-A]: Long-Term Evolution-Advanced
  *[MRs]: moving relay
  *[UEs]: user equipment
  *[BF]: beamforming
  *[DL]: downlink
  *[RA]: receive antenna
  *[NCP]: non-cooperative
  *[HARQ]: hybrid automatic repeat request
  *[E2E]: end-to-end
  *[SNR]: signal-to-noise ratio
  *[RAs]: receive antenna
  *[PAs]: predictor antenna
  *[MRT]: maximum ratio transmission
  *[NR]: new radio
  *[UE]: user equipment
  *[DAPS]: dual active protocol stack
  *[IAB]: integrated access and backhaul
  *[V2X]: vehicle-to-anything