modeling for train-ground communication channel based on wsn

7/26/2019 Modeling for Train-ground Communication Channel Based on WSN

1/5

Modeling for Train-ground Communication Channel Based on WSN

Xiaojun Lv1, Jianyu Li2, Xinchun Jia2, Bo Yang2

1Institute of Computing Technology, China Academy of Railway Sciences, Beijing 100081, China2School of Mathematical Sciences, Shanxi University, Taiyuan 030006, China

E-mail: [email protected]

Abstract: In this paper, wireless sensor network (WSN) is deployed along the high speed railway to monitor around en-vironment and to communicate with the high speed trains, which will improve the security of high speed rail significantly.

Therefore, modeling a communication channel between the train and the ground nodes has very important significance.

In this paper, a deployment scheme of wireless sensor network on a viaduct is first given, and then, considering the u-

nique characteristics of the high speed train runs on the viaduct, the train-ground communication channel is modeled as a

finite-state Markov chain (FSMC). Unlike most existing channel models, which divide the location extent of a train inton

non-overlapping intervals uniformly, in our channel model, the intervals are first divided based on path loss model. And

then, we partition each interval uniformly into some smaller intervals. The simulated data of communication channel

signal-to-noise ratio (SNR) is produced by MATLAB, and the accuracy improvement of channel model is illustrated by

comparing with an existing channel model.

Key Words:wireless sensor network (WSN), finite-state Markov chain (FSMC), signal-to-noise ratio (SNR), free space

path loss model

1 INTRODUCTION

With the rapid increasing of global population, the rail-

way transportation industry has also achieved great de-

velopment. Concomitantly, the security issue of high

speed trains has been paid more and more attention.

Communication-based train control (CBTC) system has

been widely used because it can improve the performance

of trains effectively [1]. However, the safety of the train

operating environment cannot be monitored by CBTC sys-

tem, including the damage extent of viaduct, external en-

vironment temperature of high speed trains and so on,which brings increasingly prominent train security prob-

lems. Therefore, we want to deploy wireless sensor net-

work (WSN) along the high speed railway to monitor the

railway information. And we deploy the sink nodes to col-

lect the information from sensor nodes and transmit them

to the train. Consequently, the train can get the real-time in-

formation of the around environment, which promotes the

safety level of the running train.

The technologies about WSN in high speed railway have

been studied [2]-[4]. The authors in [2] proposed an early

warning system of new arriving train based on WSN. A

beacon-driven wake-up scheme for train localization using

WSN is given in [3]. In [4], the safety of freight trains havebeen improved by using WSN. However, these literatures

do not consider the train-ground communication channel

model.

Modeling the channels of train-ground communication

is very meaningful for the deployment of sensor nodes and

This work was supportedin part by theNatural Science Foundation of

China under Grant (U1334210, 61374059), and in part by the Internation-

al S & T Cooperation Program of Shanxi Province, China (2013081040)

sink nodes. There are some previous works on train-ground

communication channel model based on radio wave prop-

agation [5], [6]. A train-ground communication channel

model based on tunnel experiment is given in [5]. Based on

the Winner II physical-layer channel model parameter, [6]

presented a channel model for high speed railway. Howev-

er, all of them divided the location extent of a train uniform-

ly inton non-overlapping intervals, and then modeling thechannel in each interval. In this paper, we partition the lo-

cation extent of a train based on free space path loss model

[7] to improve the accuracy of channel model.

In this paper, we develop a finite-state Markov chain (F-SMC) model for the communication channel between sink

node and high speed train. FSMC model has been widely

used in the modeling of communication channel, such as

indoor channels [8], Rayleigh fading channels [9], Ricean

fading channels [10], and Nakagami fading channels [11].

And a lot of analytical results of system performance can

be derived by using FSMC model [12].

To the best of our knowledge, FSMC modelling for com-

munication channel between sink nodes and high speed

train has not been studied in the previous works, which mo-

tivates current study. The main contributions of this paper

are as follows.

The location of a train is partitioned based on free s-

pace path loss model, which improves the accuracy of

channel model.

The improvement in accuracy of the proposed FSM-

C model is illustrated by comparing with the model

given by [5].

The rest of this paper is organized as follows. Section

2 describes the deployment scheme of sensor nodes and

5241978-1-4799-7016-2/15/$31.00 c2015 IEEE


2/5

sink nodes on the viaduct. The FSMC model is introduced

in Section 3. Then, in Section 4, some simulation results

of the proposed channel model are presented to illustrate

the improvement of our model in accuracy. Finally, the

conclusion and future works are given in Section 5.

2 THE DEPLOYMENT SCHEME OF WIRE-LESS SENSOR NETWORK ON THEVIADUCT

A deployment scheme of a wireless sensor network onthe viaduct is given by Fig.1, where the wireless sensor net-

work is used to monitor the damage extent of the viaduct

and around environment of the high speed rail. The yel-

low dots represent the sink nodes, and the other color dots

represent the sensor nodes, the same color dots represent

the same class sensor nodes. The sensor nodes collect the

information of viaduct and around environment and send

them to a sink node. Sink nodes receive the information

from the sensor nodes and then transmit them to the cur-

rent passing train.

Figure 1: The deployment of WSN on the viaduct

Fig.2 describes a schematic diagram of communication

between the train and sink nodes. The distances between

adjacent sinks and sink to rail are L and d0, respectively.

The distance between the train and the sink node whichis communicating with the train is d, as shown in Fig.2.The train is communicating with a sink node when it ap-

proaches to the node. And the sink node transmits the in-

formation collected from sensor nodes to the train. Signal

strength decreasing with the train away from the sink node,

and handoff happen when the signal strength is lower than

a certain threshold, namely, the train cuts off the communi-

cation with the sink node, and communicates with the next

sink node.

Figure 2: Schematic diagram of train-ground communica-

tion on the viaduct

In this paper, for simplicity, we consider the train com-

municates with only one sink node. The process that the

train approaches to the sink node and the process that the

train leaves from the sink node can be regarded as symmet-

rical, so we only consider the process that the train leaves

from the sink node. 2.4GHz Radio Frequency (RF) tech-

nology is used for the communication.

3 FSMC CHANNEL MODEL

To establish the model of the communication channel be-

tween the train and a sink node, we define channel states

according to the received signal-to-noise ratio (SNR) lev-

els and use an FSMC to track the state variation. Here, we

first introduce FSMC model, followed by the estimation of

model parameters.

3.1 FSMC model

The received average SNR is varied continuously when

the train running under high-speed circumstances because

of the effect of path loss. Obviously, the received average

SNR is high when the train is close to the sink node, where-

as it is low when the train is far away from the sink node.

Thus, the received average SNR depends on the distance

between the train and a sink node. So, it is unreasonable

to model the channel of whole location extent as a FSMC.

Here, we first divide the location extent of a train into nnon-overlapping intervals, and then model the channel as

FSMC in each interval.

In each interval, the SNR range of received signals is par-

titioned intoNnon-overlapping levels with thresholdn,n= 0, 1, . . . , N , where0andNrepresent the minimumand maximum values respectively and can be measured.

The time axis is discretized into slots, and the duration of

each slot is TF. We assume that the SNR remains same val-ue during each slot. Let(k) denote the received SNR intime slotk. Ifn1< (k)< n, then the channel state isSn at timek. As a result, the variation of the channel statecan be modeled as an FSMC. State transition probability

from statej to staten,pjn, can be shown as

pjn = Pr{(k+ 1) =Sn|(k) = Sj}. (1)

As mentioned in [5], we assume that the channel state canonly transit to the adjacent states, namely pjn = 0, if|j n| > 1. Thus, we can define a state transition matrixPwith elementspjn.

In order to distinguish the P in different internals, we usethe notion Pl as the state transition matrix in l th intervalwith elementspljn. Here,p

ljncan be formulated as

pljn = Pr{l(k+ 1) =Sln|

l(k) = Slj} (2)

wherepljn is the state transition probability from state j

to staten in thelth interval,Sln and l(k) are the channel

staten and SNR at timek in thelth interval respectively.According to the obtained data, we can get the formulas

of state probabilityplnand state transition probabilitypljn

[5]

pln = aln{

l(k) =Sln}Ni=1a

lj{

l(k) = Sli}(3)

pljn =aljn{

l(k+ 1) = Sln|l(k) =Slj}N

i=1alji{

l(k+ 1) =Sli|l(k) =Slj}

(4)

where, aln{l(k) = Sln} is the number of times that the

state Sn appears in the lth interval, aljn{

l(k + 1) =

5242 2015 27th Chinese Control and Decision Conference (CCDC)


3/5

Sln|l(k) = Slj} is the number of times that the state j

transits to staten in thelth interval.

3.2 Interval division

In this section, we present the innovation of our channel

modeling method, which also is one of the main contribu-

tions of this paper.

3.2.1 Path loss model

Due to the effect of path loss, the received average SNR

in [6] is:

(d) = PtP L(d)N0 (5)

wherePt and N0 are the transmission power and the noisepower in dBm, respectively, d is the distance between thetrain and the sink node. P L(d)is the path loss model. Be-cause the train runs on the viaduct, there are few obstacles

between the train and the sink node. P L(d) can be de-scribed as the free space path loss model [7]

P L(d) = 20lg d+ 20lg4fc

c

(6)

where fcis the carrier frequency in Hz,c is velocity of lightin vacuum. Due to the deployment of sink nodes as shown

in Fig.2, the (6) can be rewritten as

P L(d) = 20 lg(d+d0) + 20lg4fc

c (7)

wheredandd0 is the distance as shown in Fig.2.

3.2.2 Non-equality partition of the location extent of

the train

We assume that both the transmission power and noise

power are constant, we take the derivative of the formula(5) with respect to distanced

(d) = 20

(d+d0)ln10. (8)

It is easy to discover that the derivative of average SNR is

increase with the increase of the distanced.We can find that the channel fast fading models intro-

duced in [13], Rayleigh fading model, Nakagami-q (hoyt)

model, Nakagami-n (rice) model and Nakagami-m model,

whose SNR probability density function have a same pa-

rameter, namely, average SNR. If we divide the location

extent of a train uniformly, then, the range of average SNR

is large in the interval that is close to the sink node. Thus,

it is difficult to describe the fast fading model accurately in

the interval by using a average SNR.

Due to the speed characteristic of a high-speed train, few

data can be received by the train when the distance of in-

terval is small, which reduce the precision of the model in

large extent. Therefore, the length of the partitioned inter-

val cannot be too small.

Based on the considerations above, we divide the loca-

tion extent of a train as shown in Fig.3. In Fig.3, x-axis is

Figure 3: Schematic diagram of non-equality partition

the distance between the train and sink node, y-axis is av-erage SNR.i,i = 0, 1, . . . , M , are the equal partitions ofthe range of average SNR,

i = PtP L(0)N0iP L(D)P L(0)

n (9)

whereD is the maximum measured distance.

According to (5), we can get the correspondingd0, d1, . . . , dM, whered0 = 0,dM =D. So, we obtainMinternals(di, di+1),i = 0, 1, . . . , M 1, and then divideeach interval(di, di+1)intoQ smaller intervals uniformly.

diq = di +q

di+1 di

Q (10)

wherediq, q= 1, 2, . . . , Q1 represents theqth segmen-

tation point in interval(di, di+1).

3.3 Division of the SNR level in each interval

The division of SNR level in each interval is the key issue

that affects the accuracy of the FSMC model. It directly

determines the number of channel states of each interval

and to a certain extent gives the rationality of channel state

division. Here, We choose the Lloyd-Max technique [14]

to partition the SNR level in this paper.

The determination of distribution of the SNR is very im-

portant in the division of SNR level. There some classic

models to describe the fast fading channel, such as Rician,

Rayleigh and Nakagami-m [13]. Due to the trains we stud-

ied is running on the viaduct, the line of sight (LoS) path

typically exists in the multipath environment. Thus, the

fast-fading can be described using a Rician channel model

[6]. A noncentral Chi-square distribution with two degrees

of freedom can be used to describe the received SNR prob-

ability distribution function (PDF) of Rician fading channel

p(l) = Kl + 1

l exp[Kl

(Kl + 1)l

l ]

I0[

4Kl(Kl + 1)l

l ]

(11)

where Kl is the fading factor of Rician fading in lth in-terval,I0[] is the zero order modified Bessel function ofthe first kind. Both theKl and l can be estimated by amaximum-likelihood estimator.

2015 27th Chinese Control and Decision Conference (CCDC) 5243


4/5

4 SIMULATION

In this paper, to prove the improvement of our channel

model in accuracy, we compare our channel model with

the channel model presented in [5]. The mean square error

(MSE) is derived to describe the model precision.

Figure 4: Simulated data

To obtain the simulated data of channel, we assume that

D = 250m,d0 = 3m,Pt = 46dBm,N0 = 49.6dBm,fc= 2.4GHz,c= 310

8m/s. The simulated data is pro-duced by MATLAB random function as shown in Fig.4. In

each interval, we divide the channel into four states through

the SNR level thresholds. We partition the location extent

of a train into 25 intervals in both channel models. There-

fore, in the channel model [5], the length of each interval

is 10m. In our channel model, we first divide the location

extent of a train into 5 intervals based on path loss mod-

el (9), then partition each interval into 5 smaller intervals

uniformly (10). For simplicity, the length of each smaller

interval is set as an integer. Thus, the length of 5 intervals

are5m, 10m, 25m, 60m and 150m, the length of corre-sponding smaller intervals are1m,2m,5m,12m and30m.According to Lloyd-Max technique, we can get the thresh-

olds and quantized values of SNR levels in each intervals.

The quantized values is used to represent the correspond-

ing channel states. Table 1 and Table 2 give the thresholds

and quantized values of SNR levels in interval[15, 40].

Table 1: Thresholds of SNR levels in interval[15, 40]

[15, 20] [20, 25] [25, 30] [30,35] [35,40]

1st 46.2 40.9 44.0 37.8 32.1

2nd 48.2 42.4 45.4 39.8 33.6

3rd 50.3 44.0 46.9 41.9 35.0

4th 52.9 45.8 48.6 44.3 36.45th 56.1 47.7 50.6 47.1 37.9

Table 2: Quantized values of SNR levels in interval[15, 40]

[15, 20] [20, 25] [25, 30] [30,35] [35,40]

1st 47.2 41.6 44.7 38.8 32.8

2nd 49.2 43.2 46.2 40.8 34.3

3rd 51.5 44.9 47.8 43.1 35.7

4th 54.4 46.7 49.6 45.6 37.2

The state transition matrix of our channel model can be

obtained based on simulated data and the thresholds of S-

NR levels. Here, we give the state transition matrix of our

channel model in interval[25, 30]as follows

P13 =

0.56 0.44 0 0

0.24 0.47 0.29 0

0 0.29 0.59 0.12

0 0 0.33 0.67

(12)

It can be observed from the state transition matrix that the

channel state can only transit to the adjacent states, which

shows the rationality of our simulated data.

Figure 5: Simulation results generated from channel model

in [5] and the simulated results from simulated data

Figure 6: Simulation results generated from our channel

model and the simulated results from simulated data

After we get the quantized values and state transition ma-

trix, we can acquire the FSMC model of communication

channel. THe simulation results generated from channel

model in [5] and the simulated results from simulated data

are given in Fig.5. Fig.6 shows the simulation results gen-

erated from our channel model and the simulated results

from simulated data. In the two figures, the red line rep-

resents the simulated results generated from simulated da-

ta, the blue line represents the simulation results generated

from channel model.

Table 3: MSEs of 5 times simulation results

1 2 3 4 5

MSE1 13.5 5.0 1.1 3.3 19.0

MSE2 4.5 4.4 4.4 4.4 4.3

5244 2015 27th Chinese Control and Decision Conference (CCDC)


5/5

In order to better illustrate the accuracy improvement of

our channel model, MSE is used to denote the model pre-

cision. And due to the randomness of FSMC model, we

give the MSEs of 5 times simulation results. In Table 3,

MSE1 represents the MSE of channel model in [5], MSE2

represents the MSE of our channel model. From the data

of Table 3, we can obtain that the mean values of MSEs of

channel model in [5] is8.48which is larger than the meanvalues of MSEs of our channel model which is 4.4. And

we can also observed clearly that the MSE of our channel

model is more stable than the channel model in [5].

5 CONCLUSION AND FUTURE WORKS

It is very significant for evaluating the channel quality

to model the channel between the trains and sink nodes.

In this paper, we present a new distance-division approach

to model the channel. And based on simulated data, we

demonstrate the improvement of our channel model in ac-

curacy by comparing with a exiting model. Future works

are to finish the field test and to verify the improvemen-

t of our channel model in accuracy by using the field test

results.

REFERENCES[1] R. D. Pascoe and T. N. Eichorn, What is communication-

based train control, IEEE Veh. Tech. Magazine, Vol.4, pp.

16-21, Dec. 2009.

[2] Lu H, Long Z. Research of Trains Arrive Early Warning Sys-

tem Based on WSN[C]. //Industrial Control and Electronic-

s Engineering (ICICEE), 2012:International Conference on.

IEEE, 2012:1446-1449.

[3] A. Javed, H-B, Zhang, Z-Y, Huang, et al. BWS: Beacon-

driven Wake-up Scheme for Train localization using wireless

sensor networks, IEEE ICC 2014 - Ad-hoc and Sensor Net-

working Symposium, pp. 276-281, 2014.

[4] M. Gruden, M. Hinnemo, D. Dancila et al. Field operational

testing for safety improvement of freight trains using wireless

monitoring by sensor network, IET wirel. Sens. Syst., 2014,Vol. 4, Iss. 2, pp. 54-60.

[5] H. Wang, F. R. Yu, et al, Finite-State Markov Modeling for

Wireless Channels in Tunnel Communication-Based Train

Control Systems, IEEE Trans. Intell. Transp. Syst., Vol. 15,

No. 3, pp. 1083-1090, Jun.2014.

[6] S. Lin and Z. Zhong, L. Cai, and Y. Luo, Finite state Markov

modelling for high speed railway wireless communication

channel, in Proc. IEEE Globecom, Anaheim, CA, USA,

2012, pp. 5421-5426.

[7] K. Feher, Wireless Digital Communication: Modulation and

Spread Spectrum Applications, New Jersey, Prentice Hall,

1995.

[8] F. Babich and G. Lombardi, A measurement based Markov

model for the indoor propagation channel, inProc. IEEE

VTC, Phoenix, AZ, USA, May 1997, Vol. 1, pp. 77-81.

[9] H. S. Wang and N. Moayeri, Finite-state Markov channelA

useful model for radio communication channels, IEEE Trans.

Veh. Tech., Vol. 44, No. 1, pp. 163-171, Feb. 1995.

[10] C. Pimentel, T. Falk, and L. Lisboa, Finite-state Markov

modeling of correlated Rician-fading channels,IEEE Trans.

Veh. Tech., Vol. 53, No. 5, pp. 1491-1501, Sep. 2004.

[11] C. Iskander and P. Mathiopoulos, Fast simulation of diversi-

ty Nakagami fading channels using finite-state Markov mod-

els, IEEE Trans. Broadcasting, Vol. 49, No. 3, pp. 269-277,

Sep. 2003.

[12] P. Sadeghi, R. Kennedy, P. Rapajic, and R. Shams, Finite-

state Markov modeling of fading channelsA survey of prin-

ciples and applications, IEEE Signal Process. Mag., Vol. 25,

No. 5, pp. 57-80, Sep. 2008.

[13] M. K. Simon and M.-S. Alouini, Digital Communication

over Fading Channels.. Hoboken, NJ, USA: Wiley, 2005.

[14] S. Lloyd, Least squares quantization in PCM,IEEE Trans.

Inf. Theory, Vol. 28, No. 2, pp. 129-137, Mar. 1982.

2015 27th Chinese Control and Decision Conference (CCDC) 5245

modeling for train-ground communication channel based on wsn

Documents