5G for remote driving of trains

Yamen Alsaba1[0000−0001−6165−3265], Marion Berbineau2,1[0000−0003−3807−9669],Iyad Dayoub3,1, Emilie Masson1, Gemma Morral Adell4, and Eric Robert5

1 Railenium, yamen.alsaba.railenium.eu2 COSYS, University Gustave Eiffel, F-59650 Villeneuve d’Ascq-Frannce

marion.berbineau@uni-eiffel.fr3 UPHF, iyad.dayoub@uphf.fr

4 SNCF, gemma.morral-adell@sncf.fr5 Thales GT, eric.robert@thalesgroup.com

Abstract. Automatic Train Operation (ATO) is a new growing mar-ket in the railways sector since 2019. Several railways operators suchas SNCF, DB, SBB and so on have launched deep transformation intheir infrastructures and rolling stock in order to enter in digital era.One of this game changers is upgrading to autonomous train on exist-ing or new infrastructure. Since autonomous train means the absenceof train driver, there is a big need of uplinked information to superviseautonomous trains from the ground. This includes the need of remotelydriving the train if it encounters a problem, e.g. a non-recognised obsta-cle, an infrastructure breakdown. Remote driving will be a new operationmode in the railways sector that will rely on a well-designed radio linkprovided by 5G. This paper presents preliminary results on test trackswith Long Term Evolution (LTE) and a simulation based comparisonbetween LTE and 5G at physical layer using Non orthogonal MultipleAccess.

Keywords: autonomous trains, remote driving, 5G, OFDM, NOMA

1 Introduction

Full automation of trains will allow increasing drastically infrastructure capac-ity, optimizing train operations in general and also speed of the trains. Safety,security and passenger service issues are also targeted namely punctuality, trainaccording to demand, etc. Driverless system already exists in the urban segmentwith full automation of train operation on dedicated lines. The next challenge,is now to generalize automation to other railway segment such as freight, re-gional and main lines with possibly mix of traffic between trains with driver anddriverless trains. In this context, a mandatory brick relies on the remote drivingof trains.

The demonstration 6 and development of such a brick is the aim of the TC-Rail project, a partnership formed by SNCF, Thales, Actia Telecom, CNES and

6 TC-RAIL demo: https://www.sncf.com/fr/groupe/newsroom/teleconduite-train-autonome [Last accessed 12th July 2020]

2 Y. Alsaba et al.

Railenium, that will demonstrate the possibility of driving a locomotive safelyfrom a remote location, without a driver in the train cabin, with a level ofsafety similar to that obtained in presence of a driver in the train. This projectconstitutes the first proof of concept for telecontrol of a train without EuropeanRailway Management System (ERTMS) infrastructure and at maximum targetspeed of 100 km/h. It is foreseen to remove the main technical obstacles thatcould prevent such exploitation.

As the driver is no longer in the cabin, video of what the train perceivesin front of it associated with other perception information is transmitted to adistant site where is the driver, in what so called ”the eyes of the train”. Thetrain-to-ground transmission link will therefore have to be very high data ratewith a high quality of service so that the remote driver can have a vision similarto that which he would have if he were in the cabin of the train. Consequently,remote driving is based on three major technological blocks: a good perceptionsystem able to combine video with other information (audio, localization, etc.), ahigh data rate, robust, reliable and ultra-low latency radio communication uplinkbetween the train and the remote site and a remote driving Human MachineInterface (HMI) with ergonomics suitable for a new job position in railwayssector, i.e. remote train driver. There are three main applications for remotedriving of trains:

• the management of sectors between yard and the client’s site called “lastkilometers” to reduce prolonged periods of transportation and waiting timefor the drivers;• the management of technical routes between maintenance centers and sta-

tions;• the recovering from an autonomous train (failing or not).

In this paper we focus on the wireless link of the remote driving of the train.The rest of the paper is organized as follows: Section 1 gives a brief state ofthe art related to wireless communication for railways. Section 2 summarises theresults obtained during the preliminary experiments performed with 4G/ LTE.In Section 3, we propose a comparison between LTE and 5G at physical layerusing Non Orthogonal Multiple Access (NOMA) in the case of two trains in thecell. Finally we conclude and give perspectives.

2 Wireless communications for railways

GSM-R is used in the European railway sector for the control and command ofhigh speed trains. Based on GSM phase2+ system, its date of obsolescence ispredicted by 2030. To anticipate, a new system called Future Railway MobileCommunication System (FRMCS) is under development and preliminary specifi-cations have been published [8]. The most important and mandatory characteris-tics of this new system are: Internet Protocol (IP) based communications, beareragnostic, flexibility and resilience to technological evolution. FRMCS should sat-isfy all the needs for existing critical communications but also new ones related

5G for remote driving of trains 3

to driverless trains, virtual coupling and decoupling of trains, and communica-tions with sensors along the tracks. In addition, non-critical applications suchas real time video calls, augmented reality data communication, and wirelessinternet on-train for passengers, should be supported [11]. The Long Term Evo-lution (LTE) system has been particularly studied [6, 9] as a serious candidateto replace GSM-R associated with other radio access technologies (RAT). How-ever, the fact that FRMCS requires higher data rate and higher bandwidthsdue to real time HD video transmission for remote driving of train for example,has pushed researchers and industry to envision the 5G wireless communicationsystem as another alternative [1, 12]. Furthermore, in the European Shift2railprogram, prototypes of a new Adaptable Communication System (ACS) are un-der development by industry [3]. The aim is to combine different RAT e.g., 4G,5G, Wi-Fi and Satellite communications that will cooperate to provide the re-quired communication needs of the different safety and non safety related railwayapplications.

3 Preliminary experiments on the tracks

In the framework of the TC-Rail project, an LTE infrastructure in Time Divi-sion Duplex (TDD) mode was deployed specifically on a small area (around 4km) covering a portion of a french line in Paris region in order to evaluate theperformance of this dedicated technology for the remote driving of the train.It was deployed using eNodeB products from Nokia at 2.6 GHz with 20 MHzbandwidth. The masts were located at 15 m above the ground level and near thetracks. The preliminary tests were done in Single Input Single Output (SISO)configuration. The maximal transmitted power was 43 dBm and the antennasoffered a gain of 16 dB. The radio coverage was performed by SNCF Reseauteams along the tracks in order to optimize connectivity along the trip. Themeasurements were done using a non GBR (Guaranteed Bit Rate) bearer witha QCI (QoS Class Identifier) equal to 7. The results showed an average datarate of 7.7 Mbits/s in uplink in the covered area. This is very satisfying as theminimal requirement was 2 Mbps for video. The round trip latency was 60 msin average. Fig. 1 shows the train used for the demonstrator equipped withcameras, antennas and modem, and the remote driver cabin developed in theproject.

4 5G and LTE Comparison

4.1 Context

Under the umbrella of the 5G and beyond wireless communication systems,many enabling technologies have emerged recently that offers different improve-ments to 4G systems [2]. Among them, a first enhancement concerns the highsystem throughput offered by the Non-Orthogonal Multiple Access technology(NOMA). In multiple users scenarios, the users access the radio network by

4 Y. Alsaba et al.

(a) Remote driving cabin (b) Testing train on the tracks

Fig. 1: The TC-Rail experimentation

sharing the available time and frequency resources. Conventionally, users willbe allocated different time and frequency resources in an orthogonal mannersuch as in the Time Division Multiple Access (TDMA) and the Frequency Divi-sion Multiple Access (FDMA) techniques. Recently, NOMA technology [7] hasgained a widespread interest due to its accompanied gains in the overall systemthroughput. NOMA technique allows users to share the same time and frequencyresources by adopting a superposition coding schemes at the transmitter and asuccessive interference cancellation schemes at the receiver. This proved to bringcritical improvements in terms of the achieved sumrate of the corresponding usersat the cost of additional interference components that needs to be consideredwithin the transceiver design process.

NOMA has been recognized as the potential multiple access scheme for futurecommunication systems. By virtue of exploiting power domain, NOMA can servemultiple users at the same time, frequency, and code resources yielding higherspectral efficiency. NOMA communication system implementation involves twomajor processes namely Superposition Coding (SC) and Successive InterferenceCancellation (SIC) at the base station and users terminals, respectively. NOMAusers are distinguished according to their channel status, wherein users are al-located with portion of power inversely proportional to their channel condition.To decode their own messages, NOMA users suppress the information messagesof all weaker users, while considering the information of the stronger users asinterference.

Most of the literature on NOMA based communication systems has consid-ered Perfect SIC (PSIC) process, i.e. an accurate knowledge of all weaker usersinformation messages is available at the each user’s terminal. However, this as-sumption is not practical in the TC-Rail project as it implies that the user shouldperfectly estimate both the amplitude of all weaker users waveform [5]. More-over, this task becomes extremely challenging in doubly selective channels suchthose encountered for vehicular and railway wireless communication systems.A few literature can be found on imperfect SIC based NOMA system. NOMAversus OMA based systems’ performance comparison has been carried out in

5G for remote driving of trains 5

the literature for different scenarios. NOMA superiority over OMA is proved interms of users fairness [10], multi-user capacity [15], beamforming aspects [4],and cell-edge user data rate [14]. Numerical simulations illustrate that NOMAscheme provides higher data-rate, higher spectral efficiency, lower latency. How-ever, NOMA users suffer from inter-user interference.

To be compliant with the 3GPP 5G Phase 2 (release 16) [16] that adoptsNOMA as the potential multiple access candidate for the 5G systems, NOMA hasbeen adopted as the multiple access scheme for the TC-Rail project. It is worthmentioning that Orthogonal Frequency Division Multiple Access (OFDMA) hasbeen adopted in the LTE and 3GPP standard release 8 as the multiple accessmethod.

In order to illustrate the gain provided by the proposed 5G-based communica-tion systems, a comparison with LTE-based system is carried out at the physicallayer only, where no higher layers techniques are involved. As the 5G physicalimplementation is not realized yet, and important parameters such as carrierfrequency and bandwidth are not identified especially in the railway system, theTC-Rail implementation configuration and physical parameters are adopted toperform the comparison. Table 1 illustrates the adopted setup configuration inthe comparison.

Table 1: TC-Rail Setup Configuration ParametersParameter Value

Carrier Frequency 2585 MHz

Bandwidth 20 MHz

Transmit Power 20 watts

Antenna Gain 16.5 dBi

Number of OFDM Subcarriers 1200

OFDM Subcarrier Spacing 15 kHz

Trains Velocity 100 km/h

Trains number 2

Channel Model and Doppler Vehicular A, Jakes

Monte-Carlo Simulation Realization Number 1000

The physical layer technologies in the LTE setup are 3GPP release 8 compli-ant, wherein the OFDMA, OFDM, turbo coding are the technologies used forthe multiple access, waveforms and channel coding blocks respectively. OFDMis adopted for both LTE and 5G communication systems. However, the OFDMparameters in terms of number of subcarriers, subcarriers spacing, symbol andCP length are different in the 5G than its values in the LTE, as the bandwidthin the 5G is 100 MHz for operating frequencies below 6 GHz and 400 MHz at28 GHz and above where the bandwidth is 20 MHz in the LTE. As we considerthe TC-Rail LTE-based setup and for the sake of fairness, the OFDM parametersare kept the same in the LTE and 5G systems. Furthermore, the same MIMO

6 Y. Alsaba et al.

(Multiple Input Multiple Output) scheme is used for LTE and 5G systems toguarantee the comparison to be fair as choosing different MIMO schemes willchange radically the system performance in terms of throughput and Bit errorrate (BER). Table 2 summarizes the technology used for both LTE and 5G inthe comparison.

Table 2: 5G and LTE TechnologiesTechnology LTE 5G

Multiple Access OFDMA TDD NOMA

Waveform OFDM OFDM

MIMO 2*2 Alamouti 2*2 Alamouti

Channel Coding Turbo Coding LDPC

The simulation involves two served trains at the same time, a train with goodchannel condition (center train) and the second one with poor channel condition(edge train). The comparison between the LTE and 5G is carried out in termsof sum rate (the sum of the both trains rate) and BER with considering thesimulation parameters and technologies illustrated in tables 1 and 2.

4.2 System Model

We consider a communication system, wherein a base station is communicatingwith two moving users at the center and the edge of the cell. Due to the mobilityof the users, doubly selective fading channel model is adopted. The transmittedusers’ messages are first mapped into a 2-dimensional space “a time frequencyspace”, then transformed into the signal space via the synthesis function gm,k(t).Hence, the transmitted signal can be expressed as follows:

s(t) =

K−1∑k=0

M−1∑m=0

gm,k(t)xm,k (1)

where xm,k is the transmitted message at the mth subcarrier and the kth timedomain symbol .K denotes the number of time domain symbols and M is thenumber of subcarriers of the whole transmission block. The synthesis functiongm,k(t) that maps xm,k into the signal space can be written as follows:

gm,k(t) = ptx(t− kT )ej2πmF (t−kT ) (2)

where ptx(t) is the pulse shape, also known as the prototype filter. This pulseshape will determine the energy distribution (in time and frequency domains)of the transmitted symbol. T is the symbol duration while F is the subcarrierspacing. Hence, we can read Eq. 2 as follows: gm,k(t) is considered as the proto-type filter ptx(t) with translation of kT and modulation of mF .

5G for remote driving of trains 7

After conducting the sampling process, Eq. (1) be represented in matrix forms = Gx as [13], where

G = [g1,1g2,1...gM,1g1,2...gM,K ] (3)

x = [x1,1x2,1...xM,1x1,2...xM,K ]T (4)

By sampling gm,k(t) in Eq. (2), the samples are grouped in one vector gm,k ∈CN×1 where gm,k = G(:,mk). N denotes the number of samples of the wholetransmission block.

In OFDM-based NOMA techniques, super-positioned coding is applied tosend edge and cell users symbols while sharing the same time and frequencyresources. In other words, at the mth subcarrier and the kth time symbol, thesent message is written as follows:

xm,k =√αdedgem,k +

√1− αdcenterm,k (5)

dedgem,k, dcenterm,k ∈ C are the transmitted symbols of the edge and the cen-ter user, respectively. dm,k ∈ R is the possible special case e.g., Pulse AmplitudeModulation (PAM). α and 1−α are the power allocation factor for NOMA edgeand center user respectively and hence the transmitted power is normalized; i.e,E[|xm,k|2

]= 1.

In the OFDM-based OMA case, different time and frequency resources areallocated to the center and edge users, where

xm,k =

dedgem,k, (m, k) ∈ Ωedgedcenterm,k, (m, k) ∈ Ωcenter

(6)

We consider a fair distribution of resources among users, i.e. |Ωedge| =|Ωcenter| = MK/2. where |Ω| indicates the number of elements in the set Ω.

At users terminals, the demodulated signal can be written as follows:

y = QHG︸ ︷︷ ︸STM

x+Qη (7)

where STM = QHG is the corresponding System Transmission Matrix. Thenon-diagonal elements of this STM represents the interference components, whilethe desired signal dwells its diagonal elements. In the NOMA case, inter-userinterference exists even in the diagonal elements. However, at the center user,SIC is implemented to remove the effects of the edge user interference.

4.3 Sum rate

The sum rate, which represents the overall data rate at both edge and centerusers, is written as follows: Rsum = Redge+Rcenter where Redge, Rcenter expressthe data rate of the edge and the center user, respectively.

8 Y. Alsaba et al.

NOMA case: we assume that each time frequency resource has the same datarate. Hence, the data rate at user u ∈ edge, center is given as:

Ru = γM

Tlog2 (1 + SINRu) (8)

In Quadrature Amplitude Modulation (QAM) based waveforms and for OFDM,we give γ = 1. In order to calculate the SINR of the ith symbol, we start bywriting the ith row of the corresponding STM as follows

D(i, :) = Q(i, :)HG =

(GH

L−1∑l=0

diag(qHi,l)hHl

)H(9)

where diag(x) is the diagonal matrix of the vector x. We calculate the covariancematrix C = E

[D(i, :)HD(i, :)

]as:

C = GH

(L−1∑l=0

Rql Rhl

)G where Rql = qHi,lqi,l (10)

At the edge user: we first need to calculate the power of the useful signalPedge = αC(i, i) , the power of the inter-user interference PInter = (1− α)trC,the power of the intra-user interference PIntra = α (trC − C(i, i)) and hence:

SINRedge =Pedge

PIntra + PInter + Pn(11)

where Pn is the noise power.

At the center user: the SIC process will eliminate the inter user interferencecomponent, we write Pcenter = (1−α)C(i, i) and PIntra = (1−α) (trC − C(i, i)),and hence:

SINRcenter =Pcenter

PIntra + Pn(12)

OMA case: in the OMA case, users won’t share same time and frequencyresources. By assuming fair distribution of resources among users, the bit rateat each user is given as:

ROMA = γM

2Tlog2

(1 + SINROMA

)(13)

where the prelog factor of 1/2 in the rate equation is due to the fair resourceallocation between the two users. This implies also that POMA

n = (1/2)PNOMAn

5G for remote driving of trains 9

4.4 Simulation results

Fig. 2a illustrates the sumrate of the proposed 5G communication system andLTE for different waveforms. The rate is almost doubled in the 5G case withtwo trains due to the non-orthogonal resource allocation in 5G NOMA whilethe trains need to share the time and frequency resources in LTE. However,NOMA users suffer from inter-user interference that makes the gain in sumratebetween 5G and LTE nonlinear. The curves shown in Fig. 2b represents theBER performance for different multi-carrier waveforms in LTE and 5G commu-nication systems, namely OFDM, Filter Bank Multi Carrier (FBMC), FilteredOFDM (FOFDM) and Weighted Overlap and Add (WOLA). The BER of allwaveforms in the 5G is better than that of the LTE. This is due to the factthat non-orthogonal resources allocation in 5G is more robust toward chan-nel impairments especially in the railway environment, where the orthogonalitydoesn’t hold in the selective fading channel resulting from the mobility. Fig. 3draws a 3D representation of the system sumrate in LTE and 5G system as afunction of both trains’ velocity. We can notice from the figure that the 5G sys-tem maintains its superiority over LTE even in high speed regime. The sumratein 5G varies with speed between 180 and 160 Mbps at the highest speed for bothtrains, however the sumrate in the LTE case is between 100 and 80 Mbps. Hence,the 5G system is more robust against speed and the resulting non orthogonalityin the channel.

(a) Sumrate of LTE and 5G systems (b) BER of LTE and 5G systems

Fig. 2: Sumrate and BER results for LTE and 5G

5 Conclusion and perspectives

Trains are entering the era of full automation thanks to sensors and wirelesscommunications shifting control functions from the human driver to computers.

10 Y. Alsaba et al.

Fig. 3: Sumrate of LTE and 5G systems

Driverless systems already exist for metro and dedicated lines. The full automa-tion of trains in the context of existing lines with the possibility to cross othernon automatic trains is very complex. To reach this challenge, a mandatorybrick is the remote control of a driverless train from a distant site thanks toradio transmission. This will allow telecontrol of the train anywhere at any timefor example for specific maneuver in stations or marshalling yards or in case offailure of the driverless system. The TC-Rail project aims to bring a proof ofconcept of the remote control of the train. In this paper we have briefly pre-sented the evolution of the wireless communication systems for trains and thefirst performance results for the train-to-ground video transmission consideringLTE deployment along the line. Thanks to numerical simulations, we have com-pared LTE and 5G performances at physical layer with the same characteristicsin the case of two trains in the cell and we have highlighted the importance toconsider NOMA techniques associated with OFDM and MIMO to guarantee agood performances for both trains even in doubly selective channel and withhigh speed condition.

AcknowledgementsThis work has been carried out in the framework of the TC-Rail project co-

financed by a public and private consortium (Railenium, SNCF, Thales, ActiaTelecom, CNES).

References

1. Ai, B., Guan, K., Rupp, M., Kurner, T., Cheng, X., Yin, X.F., Wang, Q., Ma, G.Y.,Li, Y., Xiong, L., et al.: Future railway services-oriented mobile communicationsnetwork. IEEE Communications Magazine 53(10), 78–85 (2015)

2. Akyildiz, I.F., Nie, S., Lin, S.C., Chandrasekaran, M.: 5G roadmap: 10 key enablingtechnologies. Computer Networks 106, 17–48 (2016)

5G for remote driving of trains 11

3. Allen, B., Eschbach, B., Mikulandra, M.: Defining an adaptable communicationssystem for all railways. In: proceedings of the 7th Transport Research Arena TRA2018 (TRA (2018)

4. Alsaba, Y., et al.: Full-duplex cooperative non-orthogonal multiple access withbeamforming and energy harvesting. IEEE Access 6, 19726–19738 (2018)

5. Andrews, J.G., et al.: Optimum power control for successive interference cancella-tion with imperfect channel estimation. IEEE Transactions on Wireless Commu-nications 2(2), 375–383 (2003)

6. Brunel, L., Bonneville, H., Charaf, A.: Throughput performance of 3GPP LTEsystem in railway environment. In: International Workshop on CommunicationTechnologies for Vehicles. pp. 60–71. Springer (2017)

7. Cai, Y., Qin, Z., Cui, F., Li, G.Y., McCann, J.A.: Modulation and multiple ac-cess for 5G networks. IEEE Communications Surveys Tutorials 20(1), 629–646(Firstquarter 2018). https://doi.org/10.1109/COMST.2017.2766698

8. FRMCS Functional Working Group: Future Railway Mobile Communication Sys-tem, User Requirements Specification, FU-7100 (January 2019)

9. He, R., Ai, B., Wang, G., Guan, K., Zhong, Z., Molisch, A.F., Briso-Rodriguez,C., Oestges, C.P.: High-speed railway communications: From GSM-R to LTE-R.IEEE Vehicular Technology Magazine 11(3), 49–58 (2016)

10. Li, A., et al.: Investigation on low complexity power assignment method and perfor-mance gain of non-orthogonal multiple access systems. IEICE trans. Fundamentals97(1) (2014)

11. Moreno, J., Riera, J.M., d. Haro, L., Rodriguez, C.: A survey onfuture railway radio communications services: challenges and opportu-nities. IEEE Communications Magazine 53(10), 62–68 (October 2015).https://doi.org/10.1109/MCOM.2015.7295465

12. Mottier, D.: How 5g technologies could benefit to the railway sector: challengesand opportunities. In: Mitsubishi Electric R&D Centre Europe–France (2018)

13. Nissel, R., et al.: Filter bank multicarrier modulation schemes for future mobilecommunications. IEEE Journal on Selected Areas in Communications 35(8), 1768–1782 (2017)

14. Saito, Y., et al.: Non-orthogonal multiple access (NOMA) for cellular future radioaccess. In: 2013 IEEE 77th vehicular technology conference (VTC Spring). pp. 1–5.IEEE (2013)

15. Weingarten, H., et al.: The capacity region of the Gaussian multiple-input multiple-output broadcast channel. IEEE transactions on information theory 52(9), 3936–3964 (2006)

16. Yuan, Y., Yuan, Z., Tian, L.: 5g non-orthogonal multiple access study in 3gpp.IEEE Communications Magazine 58(7), 90–96 (2020)