Content uploaded by Yuyang Wang

Author content

All content in this area was uploaded by Yuyang Wang on May 26, 2019

Content may be subject to copyright.

MmWave Vehicular Beam Training with Situational

Awareness by Machine Learning

(Invited Paper)

Yuyang Wang‡

,Aldebaro Klautau∗

,Monica Ribero‡

,Murali Narasimha§

,and Robert W. Heath Jr.‡

‡Department of Electrical and Computer Engineering, The University of Texas at Austin, USA

∗Federal University of Para (UFPA), Belem, Brazil §Huawei Technologies, Rolling Meadows, USA

Email: {yuywang, rheath, mribero}@utexas.edu, aldebaro@ufpa.br, murali.narasimha@huawei.com

Abstract—Conﬁguring beams in millimeter-wave (mmWave)

vehicular communication is a challenging task. Large antenna

arrays and narrow beams are deployed at transceivers to exploit

beamforming gain, which leads to signiﬁcant system overhead if

an exhaustive beam search is adopted. In this paper, we propose

to learn the optimal beam pair index by exploiting the locations

and sizes of the receiver and its neighboring vehicles (parts

of the situational awareness for automated driving), leveraging

machine learning tools with the past beam training records.

MmWave beam selection is formulated as a classiﬁcation problem

based on situational awareness. We provide a comprehensive

comparison of different classiﬁcation models and various levels of

situational awareness. Practical issues are considered in realistic

implementations, including GPS inaccuracies, out-dated locations

due to ﬁxed location reporting frequencies and missing features

with limited connected vehicles penetration rate. The result shows

that we can achieve up to 86%of alignment probability with ideal

assumptions.

I. INTRODUCTION

With the huge frequency resources available, mmWave

is the only viable approach to support the massive sensor

data sharing in emerging vehicular applications [1]. MmWave

communication employs large antenna arrays to compensate

for the small antenna aperture and the penetration loss in case

of blockage [2]. With the narrow beams used in mmWave

with analog beamforming and high mobility in the vehicular

context, current beam training solutions based on exhaustive

search are inapplicable in most vehicular applications due to

the overhead and latency.

Wireless cellular communication systems have access to

vast data resources – yet untapped – which can make beam

training more efﬁcient. At every base station (BS), sector,

and radio frequency channel, hundreds of pilot signals are

sent between the base station and users in its coverage area

to measure the propagation channel every second. With the

same cadence, the users send feedback to the BS about the

measured channel, for the infrastructure to select transmis-

sion parameters accordingly. Remarkably, this information is

leveraged only over a fraction of a second and then discarded.

This “data collection - discard” mode in the wireless industry

may be explained at lower frequencies due the presence of

more propagation paths in the channel, the variability in phone

handset movement, and limitations in sensing capabilities of

phones.

Data-driven beam training is the right approach for

mmWave vehicular systems. Wireless systems are becoming

too complicated to be ﬁt in stochastic mathematical models.

MmWave channels are more deterministic given environment

geometries. Hence, beam information can be learned from the

geometry of surrounding objects, since they serve as reﬂectors

and can change directions of the beams. Also, vehicles travel

along roads in predictable ways, following certain patterns

e.g., higher density or slower speed during rush hour, which

makes it easier for mobility modeling. Vehicles are also

being equipped with sensors like satellite navigation, radar,

LiDAR, and cameras which can be used to localize vehicles

and sense the environment [1], [3]. With the availability of

sensors, vehicle locations can be shared to make everything

connected. Lastly, the BS is well equipped with the capability

to accommodate the needs for data-driven solutions. It has

access to cloud and edge computing capability; it is the natural

data fusion hub for data generated by various types of sensors

on a vehicle; it can also monitor current transmission status

and has capacity to cache the useful transmission records [4],

[5].

There are several papers proposing alternative data-driven

mmWave vehicular beam training solutions with side in-

formation, e.g., radar, cameras, etc [3]. In [6], the paper

proposed to recommend mmWave vehicular beams based on

the receiver location, with past transmission histories. The

proposed solution [6] might work well in line-of-sight (LOS)

dominant scenarios. It is very likely to fail, however, when

abundant blockages are present in the dataset, since the beams

could be highly random and large numbers of beams will

be recommended in order to guarantee alignment probability.

In [7], the paper presented a 5G-MIMO dataset of vehicular

channel and beam statistics with temporally-correlated vehicle

trajectories. An initial investigation was provided to solve the

mmWave beam selection problem when all the vehicle ge-

ometries instead of only the receiver location are incorporated

in the feature. In addition to beam pair index prediction, it

was shown in [8] that vehicles’ situational awareness can also

be used to predict the beam power to reduce overheads and

automate vehicular beam training.

In this paper, we propose a comprehensive investigation

of an efﬁcient machine learning framework with situational

awareness in Section II. Aside from using the receiver lo-

cation, we propose to locations and sizes of vehicles in

the environment to predict the optimal beam pair index. In

Section II-C and II-D, we apply three different approaches, in

Cartesian coordinates, polar coordinates and occupancy grids,

with appropriate feature ordering. In Section IV, we compare

different classiﬁcation methods to predict the optimal beam

pair index based on the situational features. Furthermore, we

show the signiﬁcant improvement of prediction accuracy when

a richer set of vehicles locations are included in the prediction.

Since the model relies on the vehicle locations, location errors

in realistic implementations are considered, including GPS

inaccuracies, vehicle location reporting frequency and different

penetration rates of connected vehicles.

II. BEAM SELECTION USING MACHINE LEARNING

A. Problem formulation

In mmWave vehicle-to-infrastructure (V2I) communica-

tions, road-side units (RSU) are deployed at a low height

(generally collocated with the lamp) on the road side, to

support data showering for the passing vehicles. Multiple

reﬂections might happen on the objects in the environment,

e.g., the road-side buildings, vehicles, pedestrians, etc, before

the beams reach the receiver. Among the different types

of reﬂecting objects, buildings (or roads) are stationary and

pedestrians are small in size which have negligible impacts

on the channel. This makes the vehicles the most important

dynamic factors in affecting the channel. We divide the set of

environment information Eto several categories: 1) stationary

objects in the environment as S(e.g., buildings, roads, other

infrastructures), 2) mobile vehicles as V, and 3) other objects

small in size (e.g., pedestrians, foliage) as N. There exists

a function f(·)that can map the set of the full environment

information Eto the beam information B, with some negligible

error η, i.e.,

∃f(·), s.t., f(E) = f(S,V,N) = B+η, (1)

where ηis due to some random disturbances in the model and

is very small.

Assuming the effect of Non the beam is negligible, and

Sis identical as S=S0for different data samples for the

current urban canyon, (1) can be further simpliﬁed to

∃f(·), s.t., f (V,S=S0,N)≈fS0(V)≈ B.(2)

Hence, we propose to learn the optimal beam pair index by

leveraging all vehicles’ locations and sizes. Speciﬁcally, the

beam selection will be formulated as a classiﬁcation problem.

In Section II-B - II-D, we will respectively explain the details

of channel modeling, optimal beam index calculation, and how

to encode vehicle geometries with appropriate feature format.

B. Data collection and channel model

Since there are no testbeds available for mmWave V2I

communications, we utilize a ray tracing simulator to establish

the dataset. Ray tracing simulation has been widely adopted in

both industry and academia for channel measurements [9], [10]

and modeling [11]. We use a commercial ray tracing simulator

- Wireless Insite, from Remcom [12]. We consider a two-lane

straight street in urban canyon as shown in Fig. 1. Buildings

modeled by cubes are located on the two road sides with

random sizes. We consider two types of vehicles, respectively

large trucks, with length, width, height =T`, Tw, Thand

low height cars (sedans) of size C`, Cw, Ch. All vehicles

are modeled as cubes with material exteriors and randomly

dropped on the two lanes based on some certain distribution.

Ray tracing simulation outputs the Lstrongest paths from

the given conﬁgurations of geometry, transceiver, etc. Since

the path directions span a three-dimensional space, the output

angles are formatted in a spherical coordinate. In particular,

each path includes: 1) arrival azimuth angle φA, 2) arrival ele-

vation angle θA, 3) departure azimuth angle φD, 4) departure

elevation angle θD, 5) path gain αejφ with φas the phase, and

6) time of arrival τ. Due to the sparsity of mmWave channel,

we assume that parameters above are enough to approximate

the channels. Let’s denote the number of transmit antenna as

Ntand that of receive antenna as Nr. The wide-band channel

H[n]with Lctaps can be calculated as below [6]

H[n] =pNtNr

L

X

`=1

g(nT −τ`)ar(φA

`)a∗

t(φD

`)α`ejφ`,

0≤n≤Lc−1,(3)

where g(·)is the pulse shaping factor, at(·)and ar(·)are

the steering vectors, and Tis the sampling period. Uniform

planar arrays (UPA) are applied at both the transmitter and the

receiver. We apply DFT codebook Wand Ffor the precoder

and combiner and assume Nt=Nr=Ny×Nx= 4 ×2=8.

Therefore, there is a total of NB=NtNr= 64 different

beam pairs in our dataset. The received power of all the NB

beam pairs in the k-th sample is represented as a vector yk.

Assuming the i-th beam pair, (wi,fi), i ∈ {1,2,· · · , NB}, is

selected from the codebook, the received power of the i-th

beam pair yk[i]can be calculated as

yk[i] =

Lc−1

X

n=0

w∗

iH[n]fi

2.(4)

and the optimal beam index skcan be obtained by

sk= arg max

i∈{1,2,··· ,|W×F |}{yk[i]},(5)

Fig. 1. Illustration of the deployment of ray tracing simulation. Receivers are

mounted on top of the low vehicles (denoted by the yellow boxes) and the

RSU is denoted by the green box.

Fig. 2. Illustration for encoding the environment with a Cartesian coordinate.

The origins lies at the left boundary of the simulation road side at the bottom.

And the each feature of the vehicle location is encoded as [horizontal location,

lane index].

Fig. 3. Illustration for encoding the environment with a polar coordinate.

The origins lies at RSU. Vehicle location is encoded as (r, φ), where φis the

angle between the point and the x-axis.

where |·| denotes the cardinality of the set.

C. Encoding vehicle geometry

To properly encode the locations of multiple vehicles,

we propose to format the features in Cartesian, polar, and

occupancy grids.

1) Cartesian coordinate: The ﬁrst approach is by represent-

ing the simulation area in a Cartesian coordinate, as shown in

Fig. 2. We are considering a two-lane street and the cars are

assumed to be traveling in the center of each lane. Categorical

variables 1 or 2 are used to denote the lane index. Each

vehicle’s location is represented by (horizontal location, lane

index).

2) Polar coordinate: Cartesian coordinate gives a way to

encode the environment in a lossless way. It includes the

accurate location of every vehicle in the environment. Another

lossless option is to use polar coordinate to encode. Beam

training is in essence a problem to ﬁnd the beam angles. In the

LOS links, polar coordinates give direct ways of interpreting

angles of the paths. To capture this angle correlation, the origin

is set at the RSU, as shown in Figure 3. And each location is

given by (distance to the origin, angular coordinate).

3) Occupancy grid: Another approach is to encode the

environment in either two-dimensional or three-dimensional

occupancy grids. This approach, though encoding the data in

a lossy way due to the quantization, can be more ﬂexible in

including a wide category of urban canyons with different sizes

of vehicles and could be more resilient to potential feature

noises. The idea is to quantize the locations in a regular-

sized grid with a certain granularity dg, which is quantiﬁed

Fig. 4. Illustration for encoding the environment in regular-sized grids. The

origin lies at the left bottom of the simulation area. The x-axis direction is

quantized to different grids to encode the data.

by the distance between two adjacent grids. The size of the

simulation area horizontally is Dh. In our model, there are

in total of 3types of target vehicles, respectively neighboring

trucks,neighboring cars, and the receiver car, represented as

1, 2, 3. An illustration is provided in Fig. 4. The grids on the

ﬁrst and second lanes will be represented by two vectors g1

and g2of length Dh

dg. If nothing lies inside the i-th grid of

the j-th lane (j= 1,2), we indicate the value gj[i] = 0; if

part of the truck lies inside the grid, gj[i] = 1; if part of a

car (excluding the receiver) lies inside the grid, gj[i]=2; if

the receiver lies inside the grid, gj[i] = 3. Finally, the vehicle

geometry vector in the occupancy grid gis concatenated by

the two vectors on the ﬁrst and second lanes into one vector

g= [g1,g2].

D. Ordering the location features

Now each vehicle is encoded in a certain format by the

three coordinates discussed in Section II-C, properly ordering

the multiple vehicles in the feature array is still required in

Cartesian and polar coordinates. Similarly as Section III-A in

[8], vehicles are ordered based on the relative distance of the

vehicle to the receiver, the type of vehicle, the lane the vehicle

is located on, etc. In brief, the feature vin each of the data

sample is a one-dimensional vector and can be represented by

v= [r,t1,t2,c1,c2].(6)

In (6), ris the location of the RSU (in the Cartesian coordi-

nate) or the location of the receiver (in the polar coordinate).

The vector tand crepresent the set of locations of trucks

and the low cars, where the subscripts 1and 2denote the lane

index. In each t1,t2,c1and c2, the vehicles are ordered based

on their relative distance to the receiver, where the details are

referred to [8].

III. PRACTICAL IMPLEMENTATIONS

The proposed beam training approach relies on a “fully

connected vehicles” assumption, i.e., we assume all vehicles

are equipped with communicating transceivers, corresponding

protocols and high-resolution GPS, which enable them to

report real-time GPS locations to infrastructures. Each time

when a vehicle initializes a beam training request, the infras-

tructure will be able to construct a real-time map of all vehicles

in the site and formulate the corresponding feature to predict

the beam based on the trained model after ofﬂine learning.

In reality, however, a lot of factors could lead to inaccurate

or missing vehicle locations in the feature. In particular,

we exemplify three important sources for noisy features as

follows.

A. GPS inaccuracy

GPS is a satellite-based radio navigation system, where

GPS satellites broadcast their signals in space with a cer-

tain accuracy. Generally, the accuracy depends on a lot of

additional factors, e.g., satellite geometry, signal blockage,

atmospheric conditions, etc [13]. Particularly in the vehicular

urban canyon with frequent signal blockages and reﬂections,

the GPS inaccuracy can be a big issue.

Since the vehicles are traveling along the center of the lanes

and lane index is simply a categorical variable, the lane index

is assumed to correct all the time. Hence, the GPS location

error is modeled by a Gaussian distribution with zero mean,

and variance of σ2. Consistently, the GPS inaccuracies will be

modeled in both the training datasets and the testing datasets,

affecting all vehicles in the features.

B. Location updating frequency

In addition to the inherent inaccuracy with the vehicle

GPS, the randomness of vehicle mobility can introduce extra

errors to the vehicle locations. Generally, vehicles report

their locations to the infrastructures every ∆ttime. With

mobility, however, the locations of vehicles stored in the

infrastructure soon become out-dated. Vehicles hence have

to report locations to the infrastructures frequently, so as to

guarantee a low level of vehicle location errors. Particularly,

given some initial setting of velocity v(t)and a(t)at t, in

the time interval [t, t + ∆t], the maximum location error is

¯x=v(t)∆t+1

2a(t)∆2

t. For simplicity, we assume the same

velocities and accelerations for all vehicles in the simulation.

The location error ¯xis applied to both training and testing

datasets.

One alternative is to pack more information in the ve-

hicles’ report to infrastructure. Connected vehicles are built

around the SAE protocol J2735 basic safety message (BSM),

which incorporates information of vehicle locations, and other

mobility related information, such as velocities and acceler-

ations [14]. With full BSM feedback, vehicle locations can

be predicted based on simple equations of motion, given the

previous state of the mobility parameters. For example, given

the vehicle’s location, velocity and acceleration at time of tas

x(t), v(t), a(t), at any time during ˜

t∈[t, t + ∆t], the updated

location can be approximated by

x(˜

t)≈x(t) + v(t)(˜

t−t) + 1

2a(t)(˜

t−t)2.(7)

As long as the reporting interval ∆tis small enough, during

which the acceleration remains stable, (7) will keep track of the

vehicle’s location precisely during the time interval [t, t +∆t].

Hence, the key is simply to capture the appropriate time

interval within which the acceleration remains. The required

reporting interval is now converted to the scale of transporta-

tion, which is several orders of the time scale of that in

communication. Also, it should be noted that exhaustive beam

search, in most of the times, cannot capture beam changes on

time, since the beam training only happens at ﬁxed time slots

t=τB,2τB,· · · (τBis the interval of conducting beam search

in traditional mmWave systems). Our model will be ﬂexible

enough to predict any potential changes of beams, since now

we have continuous interpretation of vehicle locations and

tracking of the beams based on the locations.

C. Penetration rate

Another important issue in practical implementations is

the non-ideal penetration rate of the connected vehicles.

The penetration rate deﬁnes the percentage of the vehicles

with connected devices, whose locations are known to the

infrastructures. Fully connected vehicles are not a realistic

assumption in the current state of art. Low or not 100%

penetration rate might have signiﬁcant impacts on our model

since the features have to be properly selected and ordered

correspondingly based on the locations as shown in Section

II-C and II-D. In this case, the vehicle locations might be

missing and the features might be misaligned. In particular, it

is assumed that only the low cars connected will initiate beam

training request within the proposed framework. Therefore,

among the data samples, we randomly select trucks as being

connected based on Bernoulli distribution with probability ppr,

where ppr is the penetration rate pre-deﬁned. Trucks who are

not connected will be invisible to the infrastructure and the

locations will be missing in the feature.

IV. PER FO RM AN CE EVAL UATION

In this section, we provide a comprehensive evaluation

of the proposed beam selection approach. We mainly focus

on the two performance metrics: alignment probability and

the achieved throughput. Let max{·} denote the maximum

element of the vector (·), and (·)is the indicator function.

Given the real power y1,· · · ,ym, the real optimal beam

pair index s1, s2,· · · , smand the predicted beam pair index

ˆs1,ˆs2,· · · ,ˆsmof the msamples, the alignment probability

can be formulated as

PA=1

m

m

X

i=1

(ˆsi=si),(8)

and the achieved throughput ratio RTis

RT=Pm

i=1 log2(1 + yi[ˆsi])

Pm

i=1 log2(1 + max{yi}),(9)

We start with comparing the beam selection performance

with different machine learning models. Then we evaluate

the prediction performance with different levels of situational

awareness by adding in different numbers of vehicle in the

feature. Lastly, some realistic issues of noisy features in

implementations are discussed as in Section III.

A. Learning models

For fair comparison, we start with applying a simple feature

set encoded in a Cartesian coordinate as in Section II-C, with

TABLE I

ALIGNMENT PROBABILITY AND ACHIEVED THROUGHPUT RATIO OF BEAM

SELECTION WITH DIFFERENT CLASSIFIERS.

PA(%)RT(%)

Naive-Bayes 59.31 91.14

AdaBoost 45.80 75.05

RBF-SVM 55.89 89.32

Gradient Boosting 69.05 96.49

Random forest 85.14 98.32

no GPS errors. The used classiﬁers are listed in Table I. From

the result, it is shown that random forest outperforms the

other classiﬁers, and it achieves 85.14%alignment probability.

Another important observation is that the achieved through-

put does not scale with the alignment probability. Despite

the fact that random forest achieves much higher alignment

probability compared to the classiﬁers such as Naive-Bayes or

gradient-boosting, there are no signiﬁcant differences among

their achieved throughput. The reason is the strongest several

beams’ powers are close. Therefore, even if the classiﬁer

cannot predict the exact optimal beam pair index, as long

as the selected one is among one of those top beams, the

throughput will not be too bad. The conclusion reveals the fact

that alignment probability might not be the best performance

metric for evaluation. Even though the model cannot guar-

antee 100% alignment probability, it could still be accurate

enough to identify those “good beams”. This is important

for mmWave vehicular system design, since it is beneﬁcial

to sacriﬁce some optimality to achieve low overheads. Some

sub-optimality is introduced in the prediction accuracy, but

the overheads can be largely reduced which can still provide

signiﬁcant improvement over the system performance.

B. Different levels of situational awareness

In this part, we evaluate and compare the system per-

formance with different levels of situational awareness. The

red curve in Fig. 5 plots the alignment probability with

different number of vehicles modeled in the feature. The

vehicle locations are ordered correspondingly based on relative

distance to the receiver as in Section II-D. When the feature

only includes the receiver location, an alignment probability

of around 62%. is achieved. After adding in ﬁrst lane truck

locations, the alignment probability is improved to 82%. From

the curve, it can be concluded that most of the enhancement is

contributed by the closest trucks’ (around only 2-3) locations.

Similar observations can be obtained by adding in second

lane trucks. When including low vehicles into the features,

the alignment probability remains stable which implies that

low vehicles have negligible impacts in mmWave vehicular

channels and are not very useful in predicting the beams.

Furthermore, we reduce the feature set by only keeping the

locations of vehicles that improve the alignment probability on

the red curve. Speciﬁcally, the reduced feature set becomes:

[receiver location, closest three trucks on the ﬁrst lane, closest

0 5 10 15 20 25 30 35 40 45

Number of Vehicle Locations in the Feature

0.6

0.65

0.7

0.75

0.8

0.85

0.9

Alignment Probability

Increasing feature

Reduce feature

Adding low-height carsAdding 2nd lane truck

Adding 1st lane trucks

(Nveh = 10)

Only RX location

Fig. 5. Alignment probability adding in different number of vehicle locations

in the feature. We start with only using the receiver location of the feature and

add on the ﬁrst lane truck, second lane truck, ﬁrst lane cars, second lane cars

correspondingly based on the vehicle order in Section II-D. The blue curve

is the alignment probability by only including part of the vehicle locations in

the feature, e.g., the closest several trucks and cars on each lane.

012345678910

Variance of Gaussian error

0.55

0.6

0.65

0.7

0.75

0.8

0.85

Alignment probability

0.89

0.9

0.91

0.92

0.93

0.94

0.95

0.96

0.97

0.98

0.99

Throughput

Cartesian

Polar

Grids

Fig. 6. Comparison of alignment probability and achieved throughput with

different distributions of GPS location error. Results of Cartesian coordinates,

polar coordinates and occupation grid are compared in the ﬁgure.

truck on the second lane]. Blue curve shows the alignment

probability of around 86%when this reduced feature set is

used.

C. Noisy features

In this section, noisy location features are included in the

model. The inaccuracies of the location can come from three

parts: 1) inherent GPS inaccuracy, 2) out-dated locations due

to vehicle mobility and ﬁxed location reporting interval, 3)

missing features due to limited penetration rate.

Alignment probability and achieved throughput are com-

pared in Fig. 6, with different GPS inaccuracies and encodings

of vehicle geometries. It is shown that location inaccuracy can

make a big difference on the prediction accuracy, even when

GPS error variance is very small. When the GPS error grows

large, the prediction accuracy falls below 60%. Therefore, one

limitation of our model is that it heavily relies on exact feed-

back of vehicle locations provided by GPS, whose accuracy

cannot be guaranteed, at least in the state-of-art. Also, it can

0 0.2 0.4 0.6 0.8 1

Penetration rate

0.6

0.65

0.7

0.75

0.8

0.85

0.9

Alignment probability

0 penetration rate (no situational awareness)

Full penetration rate

(full situational

awareness)

Fig. 7. Comparison of alignment probability with different penetration rates

with Cartesian coordinates.

0 100 200 300 400 500 600 700 800 900 1000

Reporting interval (ms)

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Alignment probability

a = 0, 2, 5, 10 m/s2

v = 30km/h

v = 60km/h

v = 120km/h

Fig. 8. Comparison of alignment probability with different vehicle velocities,

accelerations and location reporting intervals. Here we assume identical

uniformly accelerated motion for all vehicles and there are no GPS errors

modeled in either the training or the testing datasets for fair comparison.

be observed that Cartesian and polar coordinates achieve better

performance than the occupancy grids.

Fig. 8 demonstrates how the performance is affected when

a longer location reporting interval is applied with vehicle

mobility. We assume uniformly accelerated rectilinear motion

for all vehicles. A combination of parameters v= 30,60,120

km/h and a= 0,2,5,10 m/s2is used and a ﬁxed location error

¯x=v∆t+1

2a∆2

tis added to all features. For fair comparison,

no GPS errors are included in the datasets. It is shown

that if only the locations are reported to the infrastructures,

the alignment probability degrades very fast with a longer

reporting interval, even at a relatively low speed. Particularly,

using reporting interval of around 200 ms, the alignment

probability decreases from 86%to around 70%at v= 30km/h.

The different values of acceleration rates, however, do not have

a signiﬁcant impact on the alignment probability. Therefore, if

the velocities can be fed back to the infrastructure along with

the locations, the updated locations can largely be predicted

and the model’s accuracy can be guaranteed, with negligible

extra overheads of feedback required.

Fig. 7 plots the alignment probability with different pene-

tration rates (of trucks). Since it is assumed that all the low

vehicles report locations to the infrastructure and only trucks

might be disconnected from the network, 0penetration rate

indicates the case without situational awareness, while full

penetration rate is equivalent to the full situational awareness

case as discussed in Section IV-B. It is shown that alignment

probability scales proportionally with the penetration rate and

even at very low penetration rate of around 20%, there is still

prominent improvement of the performance compared to the

no situational awareness case.

V. ACKNOWLEDGMENT

This research was partially supported by a gift from Huawei

through UT Situation-Aware Vehicular Engineering Systems

(UT-SAVES), by the U.S. Department of Transportation

through the Data-Supported Transportation Operations and

Planning (D-STOP) Tier 1 University Transportation Center

and the Communications and Radar-Supported Transportation

Operations and Planning (CAR-STOP) project funded by the

Texas Department of Transportation.

REFERENCES

[1] J. Choi, V. Va, N. Gonzalez-Prelcic, R. Daniels, C. R. Bhat, and R. W.

Heath, “Millimeter-wave vehicular communication to support massive

automotive sensing,” IEEE Commun. Mag., vol. 54, no. 12, pp. 160–

167, 2016.

[2] Y. Wang, K. Venugopal, A. F. Molisch, and R. W. Heath, “Blockage

and coverage analysis with mmwave cross street BSs near urban

intersections,” in Proc. Int. Conf. Commun. (ICC), pp. 1–6, IEEE, 2017.

[3] N. Gonz´

alez-Prelcic, A. Ali, V. Va, and R. W. Heath, “Millimeter-wave

communication with out-of-band information,” IEEE Commun. Mag.,

vol. 55, no. 12, pp. 140–146, 2017.

[4] Y. Wang, K. Venugopal, R. W. Heath, and A. F. Molisch, “Mmwave

vehicle-to-infrastructure communication: Analysis of urban microcellu-

lar networks,” IEEE Trans. on Veh. Technol., pp. 1–1, 2018.

[5] M. Peng, Y. Li, J. Jiang, J. Li, and C. Wang, “Heterogeneous cloud radio

access networks: A new perspective for enhancing spectral and energy

efﬁciencies,” IEEE Wireless Commun., vol. 21, no. 6, pp. 126–135, 2014.

[6] V. Va, J. Choi, T. Shimizu, G. Bansal, and R. W. Heath, “Inverse

multipath ﬁngerprinting for millimeter wave V2Ibeam alignment,” IEEE

Trans. Veh. Technol., vol. 67, pp. 4042–4058, May 2018.

[7] A. Klautau, P. Batista, N. G. Prelcic, Y. Wang, and R. W. Heath, “5G

MIMO data for machine learning: Application to beam-selection using

deep learning,” in Proc. Inf. Theory and Appl. Workshop (ITA), pp. 1–6,

Jan. 2016.

[8] Y. Wang, M. Narasimha, and R. W. Heath, Jr, “MmWave Beam

Prediction with Situational Awareness: A Machine Learning Approach,”

ArXiv e-prints, May 2018.

[9] S. Hur, S. Baek, B. Kim, J. Park, A. F. Molisch, K. Haneda, and M. Peter,

“28 GHz channel modeling using 3D ray-tracing in urban environments,”

in Proc. European Conf. Antennas and Propagation (EuCAP), pp. 1–5,

IEEE, 2015.

[10] K. Haneda, L. Tian, H. Asplund, J. Li, Y. Wang, D. Steer, C. Li,

T. Balercia, S. Lee, Y. Kim, et al., “Indoor 5G 3GPP-like channel models

for ofﬁce and shopping mall environments,” in Proc. Int. Conf. Commun.

Workshops, pp. 694–699, IEEE, 2016.

[11] V. Degli-Esposti, F. Fuschini, E. M. Vitucci, M. Barbiroli, M. Zoli,

L. Tian, X. Yin, D. A. Dupleich, R. M¨

uller, C. Schneider, et al., “Ray-

tracing-based mm-wave beamforming assessment,” IEEE Access, vol. 2,

pp. 1314–1325, 2014.

[12] https://www.remcom.com/wireless-insite-em- propagation-software/.

[13] https://www.gps.gov/.

[14] Y. Zhang, G. W. Gantt, M. J. Rychlinski, R. M. Edwards, J. J.

Correia, and C. E. Wolf, “Connected vehicle diagnostics and prognostics,

concept, and initial practice,” IEEE Trans. on Reliability, vol. 58, no. 2,

pp. 286–294, 2009.