MmWave Beam Prediction with Situational
Awareness: A Machine Learning Approach(Invited Paper)
Yuyang Wang‡, Murali Narasimha§, and Robert W. Heath Jr.‡

arXiv:1805.08912v1 [cs.IT] 23 May 2018‡

Department of Electrical and Computer Engineering, The University of Texas at Austin, USA§
Huawei Technologies, USA
Email: {yuywang, rheath}@utexas.edu, murali.narasimha@huawei.com

Abstract—Millimeter-wave communication is a challenge inthe highly mobile vehicular context. Traditional beam training
is inadequate in satisfying low overheads and latency. In thispaper, we propose to combine machine learning tools andsituational awareness to learn the beam information (power,
optimal beam index, etc) from past observations. We consider
forms of situational awareness that are specific to the vehicularsetting including the locations of the receiver and the surrounding
vehicles. We leverage regression models to predict the received
power with different beam power quantizations. The result showsthat situational awareness can largely improve the prediction
accuracy and the model can achieve throughput with little
performance loss with almost zero overhead.

I. I NTRODUCTION
Configuring millimeter wave (mmWave) antenna arrays isa challenging task in vehicular applications [1], [2]. Withhigh mobility, vehicles suffer from intermittent blockages
from trucks, buses, etc, and therefore potential coverageholes, which requires frequent beam re-alignment to maintaintransmission links with high data rates [3]. Current solutions
adopted in IEEE 802.11ad is inadequate in satisfying the low
overheads and latency requirement in many vehicular applications [4], [5]. MmWave, though, is the only viable solution
to support massive data sharing and is incredibly valuable
for infotainment services, with the emerging applications in
5G vehicular/cellular communication [6–8]. Consequently, lowoverhead methods with high efficiency and robustness need to
be designed to enable fast configuration of millimeter wave
links.
One alternative to simplify beamforming in mmWave vehicular networks is to make use of out-of-band side information[9]. Side information is ubiquitous in intelligent transportation
systems. Vehicles have a number of sensors including GPS,radar, LIDAR, and cameras. In addition, connectivity between
vehicles allows exchange of such information, which equips
the vehicles with situational awareness [10], [11]. In [9],[12], out-of-band information assisted mmWave beam training
was proposed to leverage the data from sensors or othercommunication systems. In [13], it was argued that there
exists congruency between the channel at mmWave and sub6 GHz bands, which can be leveraged to do beam selection
and channel estimation. In [14], a beam alignment solution
was designed by extracting useful information from radar
signal to configure the antennas and design beams at vehicles.

An inverse fingerprinting approach was proposed to facilitate
optimal beam pair selection in [1]. Given the receiver location,
the infrastructure recommends and ranks the beams based on
the occurrences of optimal beam pair in the dataset for thatlocation. In [15], a framework of generating 5G MIMO dataset
using ray tracing was proposed, and deep learning model wasapplied to assist in mmWave beam selection with temporallycorrelated vehicle moving trajectories.
In this paper, we propose a novel framework to leveragemachine learning tools with the availability of situationalawareness, in predicting mmWave beam power. In the vehicular contexts, road side buildings and infrastructures are
stationary, and pedestrians are small in size, which makes
vehicles the most important mobile reflectors in the urban
canyons. The situational awareness of the vehicles, therefore,
can be mapped to the received power of different beams. We
propose to use the vehicle locations as features to predict thereceived power of any beam in the beam codebook, with low oralmost-zero feedback overhead. Vehicle locations may be obtained from the basic safety message in dedicated short-range
communications (DSRC), or through similar functionality ina cellular system. First, we apply different regression models
over the strongest beam power [16]. We compare the resultswith different levels of situational awareness and show thatfull situational awareness can largely improve the prediction
accuracy. We also investigate how different channel quality
indicator (CQI) quantization parameters can effect the results
using our specific dataset. We show that the optimal parameters depend on the dataset statistics and CQI quantization
does not degrade the performance much, if high resolution canbe guaranteed. Lastly, we evaluate the performance of multivariate regression over power of all beam pairs. We observethat beam selection based on the power prediction can achieve
higher throughput compared to that based on classification.
II. DATABASE ESTABLISHMENT
A. Simulation setup
In this paper, we set the analysis in a two-lane straight street
in the urban canyon, as shown in Fig. 1. Building placements
are predetermined. The road-side unit (RSU) is deployed at theroad side, at the height of 5 meters, and there are two typesof vehicles in the environments, respectively the trucks (withidentical sizes of length, height, width = T` , Th , Tw ) and lowheight cars with size of C` , Ch , Cw . We use Wireless Insitefrom Remcom to obtain the channel and beam information[17]. We only simulate the channels of low-height cars sincehigh trucks are free of blockage, and the optimal beam pair isalways line-of-sight (LOS).

Fig. 1. An illustration of the urban canyon in the ray tracing simulation.
Fixed buildings are deployed at the roadside. The green box represents the
RSU, and the receivers (yellow boxes) are mounted on top of the low-heightcars. Vehicles are randomly dropped on the two lanes, under some certain
vehicle density and truck-to-car ratio.

B. Channel model
We generate the mmWave channel by combining the outputs
from ray tracing and the geometric channel model [1]. The
rays in ray tracing are equivalent to the paths in mmWave
channel modeling. From the ray tracing output, we obtain theA
D D
path information {φA` , θ` , φ` , θ` , τ` , a` }, ` ∈ {1, 2 · · · , L}Aof the strongest L rays, where (φA` , θ` ) are the azimuth andDelevation angles of arrival, while (φD,θ`` ) are the azimuth andelevation angles of departure. And a` is the path gain for the`-th ray, and τ` is the time of arrival. We deploy 4 × 2 (Nt =Nr = 8) uniform planar arrays at both the transmitter andreceiver sides. We define g(·) as the pulse shaping filter, andapproximate the channel matrix H[n], n = 0, 1, · · · , Lc − 1by the geometric channel model
H[n] =

p

Nt Nr

L
X

A ∗ D Dg(nT − τ` )ar (φA` , θ` )at (φ` , θ` )a` ,

`=1

(1)
A
∗ D D
is the symbol period, ar (φA` , θ` ) and at (φ` , θ` )where Tare the steering vectors at the arrival and departure sides
for uniform planar arrays. We apply DFT codebook for theprecoder and combiner [18]. Therefore, there are in total of
NB = Nt Nr = 64 different beam pairs in our dataset. Assume
the i-th beam pair, (wi , fi ), i ∈ {1, 2, · · · , NB }, is selectedfrom the codebook, the received power yi can be calculaed as
yi =

LX
c −1

2

wi∗ H[n]fi .

(2)

n=0

The training label for beam power regression is y =[y1 , y2 , · · · , yNB ], and the corresponding optimal beam paircan be derived by s = argmaxi∈{1,··· ,NB } yi .
C. Power quantization
In mmWave systems, after beam sweeping is implemented,
the infrastructure cannot obtain the exact value of received

TABLE IA N EXAMPLE OF THE DATASET AND THE POST- PROCESSING

Original
CQI
Regressor
Ordered beam`i = y1 , · · · , yNB`c = Q(y1 ), · · · , Q(yNB )`r = r(Q(y1 )), · · · , r(Q(yNB ))`m (N ) = r(Q(ȳ1 )), · · · , r(Q(ȳM ))power by channel feedback. Generally, only the quantized
CQIs along with the corresponding beam pair indexes are
fed back to the infrastructure. Hence, the continuous received
power from simulation, which is obtained from (2), in firstrow of Table I and represented by `i , needs to be quantizedby some certain quantization rule. There is a vast body ofliterature discussing different mapping schemes from received
power to CQI [19], [20]. In Long Term Evolution (LTE),CQI is an indication of what modulation and coding scheme(MCS)/transport block the UE can reliably receive. Specifically, the UE determines the highest MCS for which the block
error rate is under 10%, in the bandwidth in which the CSI
reference signal is received. Our case is different from theCQI calculation in LTE, since we are not primarily concerned
about the appropriate selection of MCS at this point. Instead,
we target at predicting the beam power, and transmitting the
precise information of the received power to the infrastructure
for regression. Therefore, in our case, CQI is a direct indicatorof reference signal received power (RSRP). For simplicity, we
assume a simple uniform quantization scheme, where we use
Pu and P` to upper and lower bound the power. We then define
the CQI granularity as rCQI , and the relationship between thereceived power p and the CQI index q is defined by





Pu − P`
p − P`,0 ,, (3)Q(p) = min max
rCQI
rCQI
where the idea is to upper bound the received power by
Pu (CQI = Q(Pu )) and lower bound it by P` (CQI = 0),
and then quantize evenly for the power lying in the rangep ∈ [P` , Pu ]. And correspondingly, we recover the continuousreceived power from the CQI byr(q) = rCQI q + P` .

(4)

After CQI quantization, the entropy of the information is
reduced, and quantization inaccuracy is introduced, especiallyfor the power out of the range [P` , Pu ], which is either upper orlower bounded. In Section IV, we will show that the learning
accuracy depends on the aforementioned parameter Pu , P`
and the quantization granularity. Even with low quantization
resolution, however, we show that performance is not degradedsignificantly.
III. L EARNING MODEL
In this section, we explain the rule of encoding the situational features and the approach to predict the beam power.
We show that the power prediction is able to predict any beam,e.g., the strongest beam, second strongest beam, etc. It is alsoFig. 2. A demonstration of the vehicle’s geometry and encoding. The black
car is the receiver. The brown boxes are the small-size cars surrounding thereceiver. The green boxes denote the surrounding trucks.

applicable to the predict the beam power based on the beampair index.
A. Encoding the geometry
There are different ways to encode the vehicles’ situationalawareness. Since we deploy two types of vehicles (cars andtrucks) randomly on a two-lane street, we need to design theappropriate scheme to encode the geometry and order thefeatures accordingly. Specifically, we apply simple Cartesian
coordinate to encode the locations, as demonstrated in Fig. 2.
The origin of the Cartesian coordinate is set as the receiver, andthe road-side unit and the surrounding vehicles are encoded
accordingly under the current coordinate. Also, we can observefrom the vehicle deployments that generally for a receiver, it
is easier to be blocked and effected by the vehicle on thefirst lane, i.e., the lane closer to the RSU. Furthermore, large
trucks and vehicles that are closer by have more impact on thereceiver’s beam. Hence, we propose the following strategy to
encode and order the geometry.
The feature v is a one-dimensional vector and can begenerated by
v = [r, t1 , t2 , c1 , c2 ].

(5)

In (5), r is the location of the RSU in the Cartesian coordinate,t represents the truck and c denotes the low-height cars. The
subscripts 1 and 2 denote the lane index where the vehicle
is located on. For the trucks on the first lane, e.g., given thecoordinates of the trucks along the x-axis on the first lane in
the Cartesian coordinate as [x1 , x2 , · · · , xn ], t1 is composedof the locations of the trucks in the following order.
t1 = [xi1 , yi1 , xi2 , yi2 , · · · , xiN , yiN ],|xi1 | < |xi2 | < · · · < |xiN |.

(6)Here, we constrain the number of trucks/cars on each lane as
the maximum number of N in order to make dimensions offeatures consistent under different deployment scenarios. If thenumber of trucks n > N , we delete the locations of N − ntrucks that are located far away from the feature; otherwise, we
add in n−N virtual trucks that are lying very far away, wherex = ∞ (we choose x = 104 here). Similarly, the trucks/carson the different lanes can be encoded.
B. Practical issues with feedback
In implementations, the feedback link conveys the information of only a subset of the beam pairs. Generally, the feedback

includes the best M beams’ received power and the corresponding beam pair index. With limited information of thebeam pairs’ power, we rearrange the beam pairs in decreasingorder of their powers, i.e., ȳ1 > y¯2 , · · · , > ȳM · · · , > ȳNB ,and only apply regressor to the first M beams’ received
power, as shown in the fourth row of Table I. The model
eliminates the necessity of feeding back information of all
beams and can be combined with other models that can rank
the beams correspondingly, to achieve even lower overheads.
In this paper, we focus on the analysis of only the beam pairwith the strongest power, i.e., M = 1.
We also consider the case that unordered beam power, as
shown in the first row of Table I, needs to be fed back to theinfrastructures. Larger overheads are introduced to the system
and a longer time is required to finish the database establishment. The full knowledge of the beams’ power, however,provides an easy way to select and recommend the optimal
beam pair, and also to evaluate the system performance.
IV. P ERFORMANCE EVALUATIONIn this section, we train the regressors with different learningmodels and datasets, to predict the power of each beam. We
define the relevant performance metrics, and evaluate the system performance with different features and CQI quantization
parameters. Then we examine the performance when power ispredicted per beam pair.
A. Performance metric definition
Let max{·} denote the maximum element of the vector(·), and arg max{·} the index of the maximum value and1(·) is the indicator function. Given the real power Y ={y1 , · · · , ym }, and the predicted power Ŷ = {ŷ1 , · · · , ŷm },thePalignment probability can be formulated as PA =m
1
i=1 1(arg max{yi } = arg max{ŷi }). And we can furm
ther define achieved throughput ratio RT as
Pmlog (1 + yi [arg max{ŷi }])Pm 2
RT = i=1.
(7)
i=1 log2 (1 + max{yi })Achieved throughput ratio RT indicates the system performance in throughput when the system is deployed simply
relying on the learning model without beam training.
B. Regression models
Using the situational features defined in Section III-A, we
compare results with different regression models. We utilize
the root mean squared error (RMSE) to quantify the regression
accuracy over the strongest beam power, i.e., ȳ1 , in dB
scale. Specifically, we compare the prediction results among
linear regression, support vector regression, Random Forest
regression and gradient boosting regression in Table II. It isshown that the Random Forest is a good fit for our specificdataset, since it is able to implicitly select the features andgeneralizes well by ensembles. Also, the Random Forest is
fast to train and is a promising learning method that could beapplicable in industry field implementations.

TABLE IIR EGRESSION RMSE OVER THE STRONGEST BEAM POWER USING
DIFFERENT REGRESSION ALGORITHMS .

RMSE (dBm)
6.199
3.645
1.726
2.814

Linear regr
SVR
Random Forest
Gradient Boosting

TABLE IIIA LIGNMENT PROBABILITY AND ACHIEVED THROUGHPUT RATIO WITH
DIFFERENT CQI QUANTIZATION GRANULARITIES .

Classifier
No QT
rCQI = 1
rCQI = 2
rCQI = 5

PA (%)
84.6
82.0
81.1
79.2
72.6

RT (%)
98.4
98.8
98.8
98.7
97.8

RMSE of the Strongest Beam Power (dBm)

5

Only RX location
4.5

4Adding first lane trucks
3.5

3

2.5

Second lane
trucks2

Low-height cars

1.5
0

5

10

15

20

25

30

35

40

45

Number of Vehicle Locatoins in the Feature

Fig. 3. Comparison of alignment with different levels of situational awareness.

C. Different levels of situational awarenessIn this section, we show how situational awareness can help
to predict the beam power. Current pathloss model relies on therelative distance [8] or the absolute locations of the receiverand the transmitter [21]. Result in [1] also showed that thereceiver location only can provide useful information about
the beam by exploiting dataset of previous transmissions. Here
we show that in an urban vehicular context, the environment
information of vehicle locations could be leveraged for moreaccurate prediction of the beam power. Based on the vehicle
location order in Section III-A, in Fig. 3, we plot the RMSEof the strongest beam pair using the Random Forest regressor
with the first i-th vehicles’ locations in the feature as explainedin Section III-A. It could be observed that the first lane trucks’
locations provide abundant information about the beam power.
RMSE is reduced from 4.5 to around 1.7, compared to thecase when only the receiver location is used as the feature.
More truck locations on the second lane finally reduce the
RSME to around 1.6. Low-height cars, however, do not further
help beam prediction. Slight degradations of RMSE are shown
when extra cars’ locations are fed in the feature. Hence, we
conclude that the high trucks’ locations, especially those on
the first lane, are more relevant in predicting the beam power
and a concise set of location features are sufficient in providing
good predictions.
D. CQI Quantization
In this section, we compare the performance of the strongest
beam pair power prediction with CQI quantization. Specifically, we quantize the power first and recover the continuous
power as explained in Section II-C. We evaluate the RMSE

of prediction using different combinations of parameters rCQI ,Pu and P` . It is observed from the dataset that the highest
beam power across the dataset is Pmax = 44.23 dBm andthe lowest power is Pmin = −15.15 dBm. Based on these,
we select the combinations of Pu and P` as shown in Fig.
4. It is shown that the RMSE increases with a larger granularity generally. In the small quantization granularity regime,e.g., rCQI = 0, 0.1, 0.2, 0.5, 1 dBm, there are no significant
differences among the RMSEs. Also, the larger upper boundPu gives more accurate predictions, while the lower boundP` has negligible impact. The reason is that the power of thestrongest beam power is generally large and a small upperbound will bring large errors by quantization. It should be
noted, however, that both the upper and lower bound needs tobe carefully designed in order to guarantee the quantization
accuracy with the given statistics of the datasets.
Fig. 5 further compares the cumulative density function(CDF) of the regression error |ypred − ytrue | with different
quantization granularities. The three dots in red, blue and black
indicate the probabilities that the regression error is smaller
than 1 dBm. It is shown that with rCQI = 1 or 2 dBm, we
can still guarantee error of more than 50% of the predictions
to be smaller than 1 dBm. And there is barely any difference
between the accuracy using 1 dBm quantization and the casewithout quantization.
E. Regression over all beam pairs
Previous results apply a regression model over the strongest
beam power. In this section, we predict the power based on
the beam pair index as defined in (2). The advantage of thismethod is that it gives extra information to the order of thebeam pair power, and helps to select the beam pair for datatransmission. This case, however, requires the knowledge ofthe power of all beam pairs, which introduces larger overheads
to establish the dataset. Here, we focus on evaluating how thepower prediction can be helpful in assisting the beam selection,
without considering the overhead issues. We compare thealignment probability PA and the achieved throughput RT as
defined in Section IV-A, using different quantization granularities of CQI and the case when we simply apply a classifier
over the optimal beam pair index. Similarly as Section IV-D,
quantization with high resolution does not introduce a lot of
performance degradation. Also, even though the alignment
probabilities PA with the regression models are lower than

1
6
0.9
0.8

No quantizationr= 1 dBm

0.7

r CQI= 2dBm

0.6

r CQI = 5 dBm

CQI5

CDF of |ypred - y true |Root Mean Squared Error (dBm)

5.5

4.5

4

3.5

3

0.5
0.4
0.3
0.2

2.5

0.1

2

0
1.5

0
0

1

2

3

4

5

6

7

8

9

10

1

2

3

4

5

6

7

8

9

10

Quantization Granularity (dBm)

Regression Error |ypred - y true |Threshold (dBm)

Fig. 4. Comparion of RMSE using different selections of quantization
parameters Pu , P` and rCQI . Specifically, we consider some combinations of Pu = 45, 40, 35, 30 dBm, and P` = 0, −10 dBm andquantization granularity rCQI = 0.1, 0.2, 0.5, 1, 2, 5, 10 dBm.

Fig. 5. Comparison of regression error |ypred −ytrue | with different CQI
quantization granularities rCQI = 1, 2, 5 dBm and the case without CQI
quantization. The y coordinates of the three circles (in red, blue andblack) represent the probability that the regression error is small than
1 dB.

the classifier, the achieved throughputs are higher than thatof the classification. More beams’ power provides intrinsic
information about the beam power orders. Lastly, the achieved
throughput ratios are all very high due to the fact that there
are not big differences among the power of the top beams. The
results show that our model is good at identifying the “good”
beams from the “bad” beams, even though 100% alignment
probability cannot be achieved.

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V. ACKNOWLEDGMENTS
This research was partially supported by a gift from Huawei
through UT Situation-Aware Vehicular Engineering Systems(UT-SAVES), and by the U.S. Department of Transportation
through the Data-Supported Transportation Operations andPlanning (D-STOP) Tier 1 University Transportation Centerand Communications and Radar-Supported Transportation Operations and Planning (CAR-STOP) project funded by the
Texas Department of Transportation.
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