Cellular Bandwidth Prediction for Highly Automated Driving

Evaluation of Machine Learning Approaches based on Real-World Data

Florian Jomrich

1,2

, Alexander Herzberger

, Tobias Meuser

orn Richerzhagen

, Ralf Steinmetz

and Cornelius Wille

Opel Automobile GmbH, R

usselsheim, Germany

Multimedia Communications Lab (KOM), TU Darmstadt, Germany

Computer Science Department, Technical University of Applied Sciences Bingen, Germany

Keywords:

Cellular Networks, Connectivity Map, LTE, Throughput Prediction, Machine Learning, Mobile, Vehicular.

Abstract:

To enable highly automated driving and the associated comfort services for the driver, vehicles require a

reliable and constant cellular data connection. However, due to their mobility vehicles experience signiﬁcant

ﬂuctuations in their connection quality in terms of bandwidth and availability. To maintain constantly high

quality of service, these ﬂuctuations need to be anticipated and predicted before they occur. To this end,

different techniques such as connectivity maps and online throughput estimations exist. In this paper, we

investigate the possibilities of a large-scale future deployment of such techniques by relying solely on low-

cost hardware for network measurements. Therefore, we conducted a measurement campaign over three weeks

in which more than 74,000 throughput estimates with correlated network quality parameters were obtained.

Based on this data set—which we make publicly available to the community—we provide insights in the

challenging task of network quality prediction for vehicular scenarios. More speciﬁcally, we analyse the

potential of machine learning approaches for bandwidth prediction and assess their underlying assumptions.

1 INTRODUCTION

Highly automated driving vehicles will fundamen-

tally change our personal mobility in the future. Au-

tonomous driving will enable people to spend their

time in the car with productive or relaxing tasks in-

stead of driving by themselves. This will include

ofﬁce tasks like writing e-mails, having Skype calls

and video conferences or relaxing tasks like stream-

ing music and videos. Furthermore the car itself has

to receive continuously updates regarding the current

trafﬁc situation to ensure the safety and the comfort

of its passengers. This includes the sharing of per-

sonal sensor readings between the vehicles directly

or through a data processing backend (Here, 2015),

(Lee et al., 2016), (Jomrich et al., 2017b). All these

services require a robust and well performing mobile

data connection. However due to their mobility the

vehicles are constantly experiencing a different con-

nection quality. Reasons therefore are the varying de-

ployed technology in the cellular network, its density

and the usage related available resources of the cell

towers. To ensure a reliable data connection with a

high quality of service under such ﬂuctuating con-

ditions, different concepts and techniques have been

investigated. Several researchers proposed the idea

to enhance the insufﬁcient network coverage infor-

mation offered by the providers through data, which

is collected by the vehicles themselves (Kamakaris

and Nickerson, 2005), (Nagel and Morscher, 2011),

(Kelch et al., 2013), (P

ogel and Wolf, 2015). Through

their own communication equipment the cars are able

to sense their currently experienced connection qual-

ity. By collecting and sharing this data the network

coverage maps of the cellular providers can be en-

hanced into so called connectivity maps, which poses

a much better accuracy regarding the to be expected

network quality at a certain location in the map.

Besides this concept further approaches try to pre-

dict speciﬁc key performance parameters of the over-

all network quality in a near real time fashion. There-

fore those so called online estimation algorithms only

measure the network quality, which is currently expe-

rienced by the vehicle and try to predict future values

based on the most recent measurements. One of the

most application-relevant network quality parameters

is the expected future throughput bandwidth. Many

scientists have developed different online prediction

Jomrich, F., Herzberger, A., Meuser, T., Richerzhagen, B., Steinmetz, R. and Wille, C.

Cellular Bandwidth Prediction for Highly Automated Driving.

DOI: 10.5220/0006692501210132

In Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2018), pages 121-132

ISBN: 978-989-758-293-6

121

algorithms for this value.

Within this paper we now investigate the applica-

bility of such throughput estimation algorithms for a

large scale deployment in a mobile communication

scenario as described by the highly automated driv-

ing vehicles. Thus in contrast to existing work (Lu

et al., 2015), (Jin, 2015), (Margolies et al., 2016),

we only rely on data that is easily obtainable from

low cost hardware instead of relying on specialized

measuring equipment or software tools. To make our

obtained results comparable to the work of other re-

searchers we rely on the public APIs of nowadays An-

droid smartphones. Those devices can obtain the re-

quired network quality parameters in a similar timely

resolution and accuracy as currently deployed vehic-

ular communication hardware.

Furthermore we propose and examine the idea to

combine both mentioned concepts (the connectivity

map and the online throughput estimation) together,

to achieve better throughput prediction results. There-

fore we assume and analyse whether location speciﬁc

historical measurement data can be used to optimize

the training of instantaneous throughput estimation

algorithms.

As a further contribution we give a broad overview

about Related Work in the research area of connectiv-

ity maps and online throughput estimation techniques

in the following Chapter 2.

The rest of this paper is structured as follows.

Within Chapter 3 we ﬁrst explain the general ap-

proach and the setup used to obtain the cellular mea-

surements. Furthermore the selected highway sce-

nario and the used measuring devices with their ob-

tained quality parameters are described. Outgoing

from the obtained measurements we evaluate the ef-

fects of a variety of investigated parameters on the

overall performance of throughput estimation algo-

rithms in the context of a mobile communication sce-

nario in Section 4. We summarize our obtained re-

sults and give a short outlook regarding future work

in Chapter 5.

2 RELATED WORK

To address the general challenge of continious adapta-

tion regarding changing connectivity conditions while

moving in trafﬁc, different techniques have been pro-

posed and investigated by researchers. They can

be grouped into different categories based on the

life time of data upon which they rely as well as

their achieved accuracy and prediction horizon as de-

scribed by (Bui et al., 2014). Within this work we

investigate the possibilities that lie in the combination

of two of such concepts (connectivity maps and on-

line throughput estimation algorithms). Therefore we

now give an overview of Related Work in those two

areas of research.

2.1 Connectivity Maps

The general idea behind the connectivity map is to

collect and store information about the network qual-

ity, which is experienced at certain areas of the map.

For example this can include parameters like the re-

ceived signal strength, the experienced latency and

the measured throughput bandwidth of the network.

In the context of vehicular communication the cars

are considered as probes to generate such a connec-

tivity map. They measure the network with their per-

sonal communication equipment and share such in-

formation with a data collecting backend server. This

gathered data set within the server is than used to pre-

dict the future network quality of a vehicle based on

its current location and driving direction. Most com-

monly this is achieved by taking the average of the

historical data for equally sized areas of the map and

considering those average values as the most likely to

be experienced value in future.

One of the earlier works, which proposed the con-

cept of a connectivity map is done by (Kamakaris

and Nickerson, 2005). Within their work the authors

investigated the correlation between the Received

Signal Strength Indication (RSSI) and the measured

throughput values within WiFi networks. In their

work the authors addressed the short average lifetime

of predictions that can be achieved by the connec-

tivity map based on its stored measurements. This

is due to the highly dynamic variations of the over-

all network quality over time. The data contained in

the map therefore gets quickly outdated and has to be

constantly updated by remeasuring the current situa-

tion. The authors state that the reasons therefore are

manifold, for example the ﬂuctuation of actual users

in the network and thus the current network load, but

also environmental inﬂuences like buildings.

Kelch et al. (Kelch et al., 2013), Lu et al. (Lu

et al., 2015) and P

ogel et al. (P

ogel and Wolf, 2012),

ogel and Wolf, 2015) speciﬁcally focus on the cre-

ation of a connectivity map of the cellular network

and its possibilities for vehicular usage scenarios. A

similar kind of connectivity map is also considered to

be established in our contribution.

In the work of (Kelch et al., 2013) and (Lu et al.,

2015) both authors independently from each other in-

vestigated the usability of connectivity maps to opti-

mize the overall experienced network quality of 3G

HSDPA cellular networks. Therefore both authors

VEHITS 2018 - 4th International Conference on Vehicle Technology and Intelligent Transport Systems

122

suggest to correlate the so called Cellular Quality In-

dex (CQI)

with the achieved throughput rate at dif-

ferent locations of a map. The CQI is a parameter de-

rived from the different measured signal strength pa-

rameters (Reference Signal Received Power - RSRP,

Reference Signal Received Quality - RSRQ and Re-

ceived Signal Strength Indication - RSSI) to indicate

the overall channel quality. While driving a vehicle

Kelch et al. periodically executed the download of a

large ﬁle via the HTTP protocol using the TCP trans-

port layer protocol to fully utilize the present channel

and to measure the maximum achievable bandwidth.

The Channel Quality Indicator (CQI) was collected in

parallel by polling it each second from the used cellu-

lar modem via an AT-command.

In a similar way (Lu et al., 2015) send data for a

consecutive time of 60 seconds via the UDP protocol

to obtain active measurements of the currently present

HSDPA cell. They used Nexus 5 smartphones as mea-

suring devices. The CQI values were retrieved with a

high precision timely resolution of 2-8 ms by connect-

ing those smartphones to the so called Qualcomm eX-

tensible Diagnostic Monitor (QXDM)

framework.

As a result of their investigation both authors

suggest the Channel Quality Indicator to be a good

value to predict the achievable throughput. They state

this as possible, as an exact mapping of the Chan-

nel Quality Indicator onto the reserved resources and

the used modulation scheme of the celltower is pos-

sible. Those two parameters than directly specify the

granted available bandwidth. Such an exact mapping

however is not possible any more in the more ad-

vanced 4G/LTE networks

, which are investigated in

our work. Furthermore the achievable network speeds

in 4G networks are far larger than the speeds obtained

by Kelch et al. and Lu et al., being limited in the peak

to only 7,2 Mbit/s. This makes the predictability more

difﬁcult as larger variations in the achievable through-

put rate are possible. We argue that although we ex-

pect the CQI as well to be an important parameter

in the throughput prediction, it should not be consid-

ered as sufﬁciently enough. Thus we investigate fur-

ther parameters (see Section 3) in our personal work,

which describe the overall network quality and can

be stored within the connectivity map as well. The

work of (Lu et al., 2015) further relies on the highly

accurate CQI values obtained via the QXDM toolset.

This hinders an actual large scale deployment, as such

software requires expensive licenses and might not be

easily deployable on low cost hardware, as used in our

work.

ogel and Wolf, 2012) also investigate the pos-

www.sharetechnote.com/html/Handbook LTE CQI.html

Diagnostic Monitor. http://goo.gl/ibV7g1

sibilities of a connectivity map to predict certain net-

work parameters in the vehicular context. This in-

cludes the next cell tower, to which a future handover

of the current connection will be performed, as well

as the future to be experienced bandwidth. In a fur-

ther work (P

ogel and Wolf, 2015) this gathered infor-

mation is than used to improve a variety of network

services like adaptive video streaming and the han-

dover between different network technologies (2G,

3G networks). Similiar to (Kelch et al., 2013), (P

ogel

and Wolf, 2012) collected active measurement data

from a productive HSDPA network by performing

drive tests. Furthermore both approaches only rely on

historical collected data without taking into consid-

eration currently measured quality parameters of the

moving vehicle. (P

ogel and Wolf, 2012) stated this as

a future work in their research, however did not fur-

ther investigate it in (P

ogel and Wolf, 2015).

In conclusion of this section it can be summarized

that the connectivity map is capable to improve the

overall network experience of vehicles by leveraging

its historic data to plan future network transmissions

accordingly. However due to the high timely ﬂuctu-

ation of the overall network quality as described by

(Kamakaris and Nickerson, 2005), we argue that the

data stored within the connectivity map should not

be considered sufﬁcient enough. In addition instanta-

neous measurements of the currently experienced net-

work quality should be taken into account, too. A va-

riety of contributions, which only rely on those instant

values, are presented in the following Section 2.2.

2.2 Online Throughput Estimation

As one of the earlier contributions within the ﬁeld of

instantaneous throughput prediction (Xu et al., 2013)

develop PROTEUS, a system interface, which col-

lects instantaneous network performance parameters,

such as throughput, loss rate and one way delay of 3G

networks to predict the future network performance.

The authors did not investigate the performance of

their approach regarding the more advanced 4G/LTE

networks as it is the case in our contribution. PRO-

TEUS relies on Regression trees to enable the im-

provement of services such as VoIP, video confer-

encing and online gaming. To realize its future net-

work predictions PROTEUS only relies upon the last

20 seconds of experienced network performance and

completely avoids any form of previous ofﬂine train-

ing.

(Liu and Lee, 2015) addressed this gap by rely-

ing on previously driven trace data of 3G networks

to train their online throughput estimation approach.

The estimator itself relied upon 60 average through-

Cellular Bandwidth Prediction for Highly Automated Driving

123

put samples of 5 seconds long data transmission (300

seconds in total). This data is then used to predict

the following 300 seconds of future throughput. The

authors did not speciﬁcally investigate a mobile sce-

nario, in which the measuring probes are moving,

they still support our assumption regarding the sig-

niﬁcant impact of locality by stating that their ob-

tained results have been different for varying loca-

tions, where the measurements have been performed.

As a small disadvantage their approach has to rely

upon information gained by initially transmitting data

to be able to predict the remaining transmission qual-

ity. This approach might not be feasible for only short

term transmissions of small amounts of data as it is

the case in many automotive scenarios like sensor data

uploads from the vehicles or map updates for highly

automated driving vehicles as described in our pre-

vious works (Jomrich et al., 2017b), (Jomrich et al.,

2017a).

(Jin, 2015) are one of the ﬁrst works, who inves-

tigated 4G/LTE cellular networks for instantaneous

throughput prediction. The authors used ensemble

learner to predict the future throughput and also re-

lied on the QXDM toolset described previously. Thus

they were able to obtain a large set of precise network

parameters (e.g. the number of assigned Resource

Blocks for each client), which would otherwise not be

available. The authors share our opinion, that relying

on the QXDM toolset makes a current large scale de-

ployment not possible as considered by our approach.

The currently available devices (October 2017) do not

provide such values ”out of the box”.

(Margolies et al., 2016) investigate the inﬂuence

of so called ”slow-fading” on the obtained channel

quality of 3G networks, which is experienced speciﬁ-

cally by mobile nodes (e.g. vehicles), which are mov-

ing between different cell towers. Instead of predict-

ing the future achievable throughput (as it is the case

for our work) the authors try to predict the position

of the vehicle without relying on GPS sensors. The

current location of the vehicle therefore is predicted

based on the experienced short term history of signal

quality. In a second step the estimated position seg-

ment, in which the car is currently residing, is than

queried from a connectivity map to provide the aver-

aged throughput value of this speciﬁc segment for fur-

ther transmission planning. Thus the approach does

not include any kind of online estimation for the fu-

ture throughput value. Similar to previous work all

the measurement values have been obtained in a ﬁne

granular resolution of only some milliseconds by us-

ing the QXDM tool set.

Ide and Wietfeld et al. (Wietfeld et al., 2014),

(Ide et al., 2016) propose a predictive Channel-Aware

Transmission scheme (pCAT), which tries to identify

so called connectivity hot spots of good network qual-

ity based on measuring the experienced current and

historic Signal To Noise Ratio (SINR) to improve the

overall network performance, especially the required

energy consumption for each data transmission. Al-

though the authors do not predict the actual achiev-

able throughput, they propose in their ﬁrst work (Wi-

etfeld et al., 2014) an approach that combines his-

torical measured SINR values stored in a connectiv-

ity map and currently measured values together to

identify the connectivity hot spots. Thus the gen-

eral concept is similar to the investigated idea within

our work. In their second work however the authors

then focus only on location-independent connectivity

data to feed their channel-aware transmission scheme.

Also only the Signal To Noise Ratio (SINR) is inves-

tigated as an inﬂuencing factor of the channel quality.

In our work we take additional quality parameters into

account.

(Samba et al., 2017) present another approach

to predict the currently achievable throughput band-

width in current 4G networks. Therefore they rely on

the usage of Random Forest classiﬁcation trees. In

contrast to the work of Ide and Wietfeld et al. the

authors also consider further parameters like the Re-

ceived Signal Strength Indicator (RSSI) and the Ref-

erence Signal Received Quality (RSRQ) in their ap-

proach. They also take the context of their perfor-

mance measurements into consideration, e.g. the cur-

rent distance between the device and the cellower and

its moving speed, but they do not include the exact

location. As a result they do not provide location spe-

ciﬁc training data sets to their machine learning clas-

siﬁer like it is the case for our work. Additionally

to the information collected by the device the authors

further rely on a data set provided by a cellular net-

work provider, which offers further details about the

radio access network. Samba et al. show, that this

information in combination with the achieved data

from the end user devices improve the estimation re-

sults. However we argue that such information should

not be expected to be shared on a common basis be-

tween the network providers and vehicles. Thus a

general deployment of a predicition approach that re-

lies on such data is questionable. Furthermore the au-

thors obtain their measuring data from a crowdsourc-

ing campaign in which 30 different users performed

a total of 5700 measurements in 350 different cells.

This results in a large diversiﬁcation of different loca-

tions. For our measurements we narrow our measure-

ment campaign to only a small part of the German

highway A60 to achieve a signiﬁcant high number of

measurement results in a small amount of cells as de-

VEHITS 2018 - 4th International Conference on Vehicle Technology and Intelligent Transport Systems

124

scribed in Section 3.2.

Our personal contribution is now described in the

following Section 3. We present our ﬁndings regard-

ing a throughput estimation algorithm to be used in

a purely mobile context. Therefore we investigate

possibilities to combine both presented concepts to-

gether: the connectivity map with its historic data

set regarding future locations and the online esti-

mation algorithms with their measurements of latest

achieved network quality to predict the future experi-

enced throughput.

3 CONCEPT

As the main contribution of this work we want to in-

vestigate the possible performance of online through-

put estimation algorithms in a future large scale de-

ployment. Therefore we let our investigated through-

put estimation algorithms only rely upon network

quality parameters, that can be collected ”out of the

box” from currently available (October 2017) public

APIs and end customer hardware instead of relying

on expensive diagnostic toolsets or special hardware

as frequently used in the presented Related Work. The

exact setup is described in the following.

3.1 Measuring Setup

To make our measurement results and achieved

estimation performance comparable for other re-

searchers, we used Android smartphones as measur-

ing probes. Such devices are able to provide a sim-

ilar amount of different network quality parameters

in a timely granularity, that is also achieved by cur-

rent built-in communication systems, but instead are

freely available for easier comparison.

Most of the contributions presented in the Re-

lated Work investigated their personal concepts by

probing older 3G UMTS and HSDPA networks. In

contrast to them we focused our measurement cam-

paign purely on active measurements of the current

4G/LTE network. In contrast to the work of(Samba

et al., 2017), which also investigated 4G networks, we

used newer devices of the LTE category 6 (Samsung

A3) and 9 (Samsung S7), which are able to rely on

new technological features like Carrier Aggregation

or 4x4 MIMO. The measurements of those devices

were compared with a device of category 4 (Google

Nexus 5), which cannot rely on those technical fea-

tures.

To collect the required quality indicators for the

estimation algorithms, we developed an Android ap-

plication as described in our previous work (Jomrich

Table 1: Obtained measurement parameters of the different

devices for network quality estimation.

Device

type

used features for training

Samsung

A3 / S7

Signal Strength Level, Timing Advance,

RSRP, RSRQ, CQI, RSSNR, average speed

of vehicle

Nexus 5 same as A3 and S7, but no Timing Advance,

CQI and RSSNR

et al., 2017a) that logged all available network pa-

rameters, which we could obtain from current An-

droid OS systems, while performing our throughput

measurements. Those pairs of passive obtained net-

work quality parameters and active throughput mea-

surements were then used as data for our investigation

of throughput prediction with state of the art machine

learning techniques as presented in chapter 4. De-

pending on the speciﬁc device type different network

quality parameters could be obtained via the usage of

the Android API. The Samsung A3 and the Samsung

S7 devices could provide the same feature set. The

Nexus 5 device in contrast provides only a reduced

feature set as described in the Table 1.

Based on the provisioning constraints of the An-

droid OS all described network parameters were ob-

tained with a timely resolution of up to one second.

This is a rather coarse timely resolution in compari-

son to the specialized tooling software like Qualcom’s

QXDM, which is used frequently in other Related

Work and can obtain the data within only some mil-

liseconds granularity. Thus we expect less accurate

prediction results.

Similar to the Related Work of (Lu et al., 2015) we

used the UDP transport layer protocol to perform our

throughput measurements. Therefore we modiﬁed the

described application in (Jomrich et al., 2017a) to be

able to execute continuous measurements. We se-

lected the UDP protocol as our transport layer pro-

tocol, as we wanted to be able to perform our mea-

surements as quickly as possible without wasting any

costly cellular trafﬁc data. UDP in contrast to the TCP

protocol does not rely upon protocol speciﬁc control

mechanisms like slow start and congestion control.

Thus by using UDP we could probe the upload and

download speed right from the beginning of our mea-

surement transmission. To verify, that our application

was able to congest the available cellular connection,

we used the LTE network sniffer Imdea OWL devel-

oped by (Bui et al., 2017) as a ground truth. Within

their work Bui et al. could verify that smartphone ap-

plications can reliably measure the available through-

put by decoding the LTE control channel and correlat-

ing the transmitted packets of the measurements with

the decoded results. We tested different probing set-

Cellular Bandwidth Prediction for Highly Automated Driving

125

Figure 1: Investigation of the amount of data that satu-

rates the bandwidth of one single LTE cell in the download.

Within this picture the obtained results for Provider B are

shown. They are similar for Provider A.

Table 2: Measurements executed for two different providers

with three different devices of different categories.

Provider

Device Samsung

(Cat. 9)

Google

Nexus

(Cat. 4)

Samsung

(Cat. 6)

Samsung

(Cat. 9)

Amount of

measure-

ments

36775 12834 9166 15513

Amount of

data [GB]

27,58 9,63 6,87 11,63

tings to conduct our measurements as data efﬁcient

as possible. Besides data efﬁciency also measuring

accuracy was ensured by veriﬁcation through Imdea

OWL. The achieved results of this investigation are

presented in ﬁgure 1.

As a concluding result we conducted our further

measurements throughout the testing campaign by

sending a packet train of 750 Kilobyte of data to a

dedicated server and receiving the same amount of

data via download from it. We ensured the server

to be sufﬁciently connected with around 500 MBit/s

of transmission speed in both directions. In total

an amount of 74.468 upload and download speed

measurements could be collected. The distribution

of the measurements is indicated by Table 3. We

collected measurements for two different providers,

which could provide 4G/LTE connectivity along our

complete test track. The collected data set will be

made publicly available on GitHub

. We hope this

way to provide a new benchmark data set for further

future approaches of related scientiﬁc work.

https://github.com/ﬂorianjomrich/

cellularLTEMeasurementsHighwayA60

3.2 Scenario Description

In contrast to most of the presented Related Work,

we focused our measurement campaign speciﬁcally

on a mobile scenario. Highly automated driving is

currently developed with a major interest regarding

the initial deployment on highways. To investigate

the possible usage of our concept for such a commu-

nication scenario, we selected a short section on the

Germany highway A60 for our test measurements as

shown in Figure 2. Throughout this track the smart-

phones have always been connected to 4G/LTE net-

work cells. At certain segments even 4G+ connectiv-

ity, which indicates the availability of Carrier Aggre-

gation, was available for the A3 and the S7 devices.

The measurement campaign was conducted for a pe-

riod of 3 weeks. Within the campaign we compared

the networks of two German providers, in the follow-

ing named A and B. As an interesting side constraint

all the cell towers of Provider A operated in the LTE

Band 20 at frequencies of 800 MHz. For provider B

the situation was contrary. Nearly all it’s cell towers

(with only one exception) operated in the LTE Band

3 at frequencies of 1800 MHz. The different cells

to which the smartphones have been connected are

colourised in Figure 2 with different colours.

3.3 Investigation of Machine Learning

Algorithms for Use in Evaluation

As an initial investigation, before executing our eval-

uation we compared different Ensemble Learner algo-

rithms with each other, regarding their overall perfor-

mance on our data set. Namely we investigated Gra-

dient Boost a boosting machine learning algorithm, as

well as the Random Forest as one of the representa-

tives for the bagging technique. Throughout our in-

vestigation we could not identify a certain advantage

off one of the algorithms over the other. Therefore

to be comparable with the newest Related Work of

(Samba et al., 2017) we decided to further rely in our

evaluation upon the Random Forest algorithm. The

presented results in the following Section 4 therefore

have been obtained by using the Random Forest re-

gression algorithm for the prediction of the through-

put value.

4 EVALUATION

We now present the obtained evaluation results gained

from our three weeks long measurement campaign.

To ensure a representative timely distribution in our

dataset we performed our test drives each day of the

VEHITS 2018 - 4th International Conference on Vehicle Technology and Intelligent Transport Systems

126

(a) Provider A (b) Provider B

Figure 2: Selected driving scenario for the measuring campaign. Measurements of different cells are coloured differently.

OpenStreetMap contributors.

Table 3: Median of achieved throughput values for both

providers.

Provider

Device S7 Nexus

A3 S7

Median upload speed

[Mbit/s]

13,40 13,47 13,48 13,37

Median download

speed [Mbit/s]

29,25 30,88 34,32 35,69

week, separated into two different time slots, a morn-

ing part (from 8 till 12 o’clock) and an afternoon part

(13 till 17 o’clock).

4.1 General View on the Collected Data

As the ﬁrst step of our evaluation we did a check of

the distribution of obtained throughput measurements

in the upload and download direction. Therefore we

separated the data in further sub sets accordingly to

the three different devices for each of the two different

operators, which networks have been used in the cam-

paign. The resulting histogram plots are presented

in Figure 3. With respect due to space constraints,

we only show the results obtained for Provider A.

The plots for Provider B however are very similar

and the achieved median throughput values for the

upload and download direction of both providers is

shown in Table 3. Based on those values we could

not identify any speciﬁc advantages of the newer de-

vices with LTE category 6 (A3) and 9 (S7) compared

to the older category 4 devices (Nexus 5). The medi-

ans of the upload and download are nearly the same

for the devices used for each provider. Provider B

could only achieve a small performance advantage of

around 5 Mbit/s in the median in the download com-

pared to the same device used in Provider A’s net-

work. These ﬁndings support the statement of (Jin,

2015), in which the authors identiﬁed the providers to

compensate certain technical disadvantages of older

devices through a fair scheduling trade off between

old and new devices. Older devices get assigned

more resource blocks throughout a transmission and

therefore achieve comparable data transmissions like

newer devices.

4.2 Correlation between Training Data

Size and Prediction Accuracy

The correlation between the size of the used train-

ing data set for the machine learning approach and

the resulting prediction accuracy was one of the ﬁrst

questions, which we wanted to investigate. Within all

the presented Related Work this factor has not been

discussed. It however is an important question for

scientists or companies, who want to perform simi-

lar measurements and to ensure the reliability of our

obtained results. We investigate how the prediction

accuracy is related to the actual used training data set

size and whether this correlates as well with the spe-

ciﬁc type of the device, the provider or the direction

of the data transmission. To answer this questions we

selected randomly a subset of all our measurements,

which have been conducted by the device for the cer-

tain provider and applied the Random Forest Algo-

rithm for regression on it. To ensure the signiﬁcance

of these results we repeated the process of random se-

lection and algorithm application for 30 times. In Fig-

ure 4 the obtained results are presented as box plots.

Interestingly the variance and the prediction accuracy

of all the different trained models saturated between

1500 and 2000 measurements used for training, inde-

pendent of the speciﬁc device under consideration or

the provider under test. The further improvements re-

garding variance reduction and increasing the predic-

tion accuracy, which could be achieved by using more

training data (up to 10.000 measurements), were only

minor.

4.3 Comparison between Upload and

Download Prediction Accuracy

Most of the presented Related Work focused on the

prediction of the throughput in the download direction

to optimize the user experience of applications, which

are receiving data on the users device. The most

frequently used example are adaptive video streams.

Future autonomous driving vehicles however won’t

be only consumers of data. They will be provid-

ing relevant personal sensor information to central

Cellular Bandwidth Prediction for Highly Automated Driving

127

(a) Nexus Upload (b) Nexus Download (c) S7 Upload (d) S7 Download

Figure 3: Distribution of achieved throughput measurements in the network of Provider A.

backend servers. This data will then be processed

and shared between all trafﬁc participants to further

enhance the overall safety while driving. Further-

more the passengers inside the vehicle might also re-

quest different kinds of services, which require a cer-

tain upload throughput, for example for Skype video

calls. To also support such applications we mea-

sured the achievable throughput in the upload and in

the download direction throughout our measurement

campaign.

The achieved results for the different transmission

directions are presented in Figure 4. The plots show

the achieved prediction accuracy of the Random For-

est algorithm trained for regression learning. To be

comparable with the Related Work we used the same

metric like (Samba et al., 2017), the R

value. R

deﬁned as described by Formula 1, where ¯y is the av-

erage of all considered throughput measurements, y

is the current throughput estimation and is ˆy

the pre-

dicted throughput value.

= 1 −

∑

i=1

( ˆy

− y

)

∑

i=1

( ¯y − y

)

(1)

We have to state, that with our obtained measure-

ment data set we were not able to reproduce the over-

all achieved performance of the Random Forest Algo-

rithm’s estimation accuracy as stated by (Samba et al.,

2017). The most likely reason therefore might be the

difference in the overall transmission time between

our two different approaches. Samba et al., as stated,

conducted a download measurement by transmitting

4 MB of data. In a former work of them (Samba

et al., 2016), the authors stated that in average one

of their data transmissions took about 8 seconds of

time. Within our current work however, we wanted

to keep the data transmission time as short as possi-

ble, as due to the mobility of the vehicle the cellular

network quality is also constantly changing and in-

ﬂuencing the obtained measurement result. Thus we

only transmitted an amount of 750 KByte of data per

measurement to still ensure a congestion of the data

channel as described in Section 3.1. In a stationary

scenario transmitting more data over a longer period

of time possibly compensates the short term ﬂuctu-

ations of the network, when calculating the overall

throughput. Thus the machine learning might become

more reliable, as only a long term average throughput

is being used for learning, which contains less ﬂuctu-

ation. This however might not be a feasible approach

in a pure mobile context, as investigated within our

work, in which changing quality effects due to the

mobility of the vehicle play a much more important

role. A possible trade off between those two noises

to achieve a higher precision of the throughput pre-

diction in our mobile context will be investigated by

us in future work. Another possibility to improve

the achievable performance could be to improve the

timely resolution of obtained network quality param-

eters, to get a more accurate impression of the timely

behaviour of the network (e.g by relying on QXDM).

For our current approach however this is not possi-

ble, as the interval for receiving quality parameters is

limited by the Android OS.

Still the achieved results show, as expected, that

the actual value of the obtainable download speed is

more difﬁcult to predict, than the corresponding one

for the upload speed. This holds true for all devices

and both investigated providers. The most probable

reason therefore is the more diverse distribution of

achievable download throughput values compared to

the distribution of the upload values (see Figure 3).

Due to a wider range of possible outcome values the

prediction of the machine learning approach becomes

more difﬁcult. This especially holds true for mod-

ern 4G networks, which achieve a higher bandwidth

than older 3G technology. Further possible reasons

might be the higher modulations schemes, which can

be used in the download direction or the usage of

the MIMO antenna technique for the download data

stream. Both factors might inﬂuence the overall con-

sistency of the signal quality during the execution of

the measurement.

4.4 Comparison between Different

Devices

Throughout our measuring campaign we performed

the throughput estimation with three different devices

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128

Figure 4: Comparison between the different devices for the different providers.

(Nexus 5, Samsung A3 and S7). Each device was re-

lated to a different LTE device category (4,6 and 9).

The signiﬁcant amount of measurements collected by

each device (see Table 3) enables us to investigate

a comparison between them, in contrast to most of

the presented Related Work, where all devices were

considered together. Although the distribution of the

overall achieved throughput measurements are very

similar (see section 4.1) for all the devices, the pre-

diction performance of the Random Forest Algorithm

is quite different for each of them (see Figure 4). The

data provided by the Nexus 5 smartphone achieves the

highest prediction performance, although the set of

parameters, which the Nexus 5 can provide (see Table

1) is reduced. To investigate the possible inﬂuence of

those neglected network parameters (e.g. the Channel

Quality Indicator - CQI), we investigated the perfor-

mance of the estimation algorithm based on a training

data set of the S7 device, where those parameters have

not been considered for training (see Figure 4). The

obtained results therefore however are very similar to

the full set of parameters for the S7. The performance

of the Nexus 5 device still could not be reached. Most

surprisingly the performance of the throughput esti-

mation based on the measurements obtained from the

Samsung S7 device also performed worse in compar-

ison to the achieved results of the Samsung A3 de-

vice. Even though both devices provided us the exact

same set of network quality parameters. The possible

inﬂuence of Carrier Aggregation can only be consid-

ered for the comparison between the Nexus 5 device

and the S7. Still it would only explain the difference

for the download direction, as in the upload direction

no Carrier Aggregation is used. In contrast the A3

and the S7 both relied on this technology within our

measurements. In conclusion it can be stated, that the

speciﬁc hardware built into each device seems to play

a very important role in the achievable overall per-

formance of the estimation, although the overall ob-

tained measured throughput is nearly the same. An

investigation, if a longer throughput measurement (as

described in Section 4.3) can reduce these device de-

pendent effects, shall be conducted in future work.

4.5 Inﬂuence of Local Training Data

Compared to Global Training Data

As a further idea, which we propose in this work, we

investigated if the performance of an online through-

put prediction algorithm could be improved through

the usage of cell tower speciﬁc/geo-referenced train-

ing data. Such a functionality for example could be

realized through a connectivity map. Based on the

large scale historical data contained in the map, it

could provide speciﬁcally trained Ensemble Learners

for each cell tower along the path of the vehicle to

be used by the throughput estimation algorithm. In

our opinion, instead of only relying on a generalized,

non-geo referenced training set, this might possibly

increase the algorithms performance. To examine this

Cellular Bandwidth Prediction for Highly Automated Driving

129

idea, we generated decision models based on the data

speciﬁcally obtained from the eight cells, in which

we could gather the most measurements. Those eight

cells split up into four cells per provider. All those

measurements have been collected with a Galaxy S7

as measuring device. Those cell tower speciﬁc de-

cision models were than compared to decision mod-

els of equal size, which have been generated form

data obtained from all the other cells in which we

conducted measurements. For performance compari-

son both decision models (locally trained and globally

trained) were than tested on separated test data, which

was measured in the cell tower currently under inves-

tigation. We ensured the ratio between training data

size and testing data size to be 70:30. Again we re-

peated the experiment for 30 times with randomly se-

lected measurement sub sets to ensure the signiﬁcance

of our obtained results. This time we calculated the

mean absolute error between the predicted throughput

value and the actual tested throughput value as met-

ric of comparison. Due to space we present only the

plots obtained for the four cells of Provider A. The re-

sults for Provider B were similar, but due to a smaller

amount of collected measurements per cell probably

not as representative. We certainly agree that by com-

paring only eight network cells with each other no

generalisation for the whole LTE network is possible.

However our resulting graphs show some visible ten-

dencies, which might become even more signiﬁcant if

a more precise measuring approach or network qual-

ity parameters of a ﬁner timely resolution can be used

in future work. As tendency it is visible, that in the

cells where the Random Forest algorithm performed

best or close to it, the decision tree with local training

data could improve the overall estimation accuracy

signiﬁcantly. For cells, in which the algorithm did

not perform very well in comparison, the inﬂuence

was not signiﬁcant. For one of those cells (cell D)

it even performed a bit worse in the upload direction.

The inﬂuences were more visible for the upload direc-

tion. This probably correlates again with the poorer

prediction performance, which the Random Forest al-

gorithm could achieve for the download speed based

on our collected measurement data set as discussed in

section 4.3.

In conclusion we state that our obtained results show

potential, that justiﬁes further personal research re-

garding it in future work.

5 CONCLUSION AND FUTURE

WORK

In this paper we investigate the possible performance

of online throughput estimation algorithms for the

currently deployed 4G/LTE cellular network to ensure

a reliable connectivity quality for services required in

the context of highly automated driving vehicles. We

especially wanted to examine the possible applicabil-

ity of those algorithms in a large scale future deploy-

ment on existing communication hardware currently

built into the vehicles. Therefore we base our perfor-

mance estimates on a data set, which is collected by

using Android smartphones as comparable and low

cost hardware, that can be used ”out of the box” with-

out the requirement of any further special tooling or

equipment. We obtained a measurement data set of

over 74.000 throughput measurement values for the

upload and the download direction with correlated

network quality parameters over a period of three

weeks. This data set will be made publicly available

to the community for further investigation in future

work and as a support of other related research. Fur-

thermore we give a broad overview of Related Work

in the areas of connectivity maps and online through-

put estimation algorithms to enhance the overall ex-

perienced connection quality in vehicular communi-

cation scenarios. In addition we examine the idea to

combine the capabilities of both techniques together

by training the used Random Forest machine learn-

ing algorithm with localized training data, in compar-

ison to non-geographically referenced global training

data. In contrast to Related Work we speciﬁcally fo-

cused our measurement campaign of the cellular net-

work on an automotive and mobile scenario. Based

on the obtained results we can show, that the through-

put estimation in a mobile context is rather difﬁcult

compared to a throughput estimation predicted for

a stationary scenario, as already performed in Re-

lated Work (Samba et al., 2016). A possible reason

therefore might be the contradicting inﬂuence of two

different noises. The general performance ﬂuctua-

tion within the network can be reduced by conduct-

ing measurements over a longer period of time. Un-

fortunately in our mobile communication context this

introduces additional ﬂuctuation through the move-

ment of the vehicle and the correlating changing net-

work quality. A possible tread off between those two

different noises to obtain better performance results

shall be investigated in future work. Furthermore we

could showcase the inﬂuence of different measure-

ment devices on the overall obtained estimation re-

sults for the 4G/LTE productive networks of two Ger-

man providers. For future work we will extend the

VEHITS 2018 - 4th International Conference on Vehicle Technology and Intelligent Transport Systems

130

Figure 5: Comparison of the performance of local vs global training data sets of the same size.

presented work by investigating the performance of

currently deployed communication hardware within

the vehicles themselves and compare those achieved

results with our presented results. By continuing the

collection of measurement data also the investigation

of timely aspects like changing network load over the

day and provider speciﬁc trafﬁc scheduling shall be

investigated. Therefore normalisation techniques as

described in (Gozalvez and Coll-Perales, 2013) shall

be taken into consideration. Furthermore a deeper in-

vestigation of new technological effects like the over-

all inﬂuence of Carrier Aggregation is necessary. As

the Android OS only indicates the availability of this

LTE-Advanced feature, but not its actual usage, it

is currently not possible to get further details to im-

prove the prediction accuracy. Thus our current work,

which only relies on the public available Android API

shall be enhanced in future with more in depth analy-

sis features.

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