Adaptive Transmission Scheme for Vehicle Communication System

Sangmi Moon, Sara Bae, Myeonghun Chu, Jihye Lee, Soonho Kwon and Intae Hwang

Dept. of Electronics and Computer Engineering, Chonnam National University,

300 Yongbongdong Bukgu Gwangju, 500-757, Republic of Korea

Keywords: Adaptive Transmission, Freeway, LTE-based V2X, PRR, SLS.

Abstract: Advances in Vehicle-to-Everything (V2X) communication attempt to enhance traffic safety by employing

advanced wireless communication systems. V2X communication is a core solution to manage and advance

future traffic safety and mobility. In this study, we design a system-level simulator (SLS) for Long Term

Evolution (LTE)-based V2X and propose an adaptive transmission scheme for vehicle communication. The

proposed scheme allocates the resource randomly in the time and frequency domains and transmits the

message according to the probability of transmission. The performance analysis is based on the freeway

scenario and periodic message transmission. Simulation results show that the proposed scheme can improve

the cumulative distribution function (CDF) of the packet reception ratio (PRR) and the average PRR.

1 INTRODUCTION

Communication technology has been utilized for

communication and provision of information

between people. However, in recent years, the

application of this technology has been expanded for

device-to-people and device-to-device communication.

In particular, vehicular communication (V2X:

vehicle-to-everything) has many applications,

including navigation and driver assistance, travel

information, congestion avoidance, fleet

management, payment transactions, and traffic

control and safety.

Figure 1: Types of V2X communication.

As shown in Figure 1, V2X communication may

occur in multiple contexts: vehicle-to-vehicle (V2V)

communication, vehicle-to-pedestrian (V2P)

communication, and vehicle-to-infrastructure (V2I)

communication. These applications are referred to as

Intelligent Transport Systems (ITS). V2X applications

range from personal communication and green

transportation to societal mobility and safety in order

to increase travel convenience, comfort, and safety.

V2X applications can be supported by two main

communication classes: cellular-based communica-

tion systems (e.g., Long Term Evolution (LTE)) and

Wi-Fi-based communication (e.g., 802.11p or

802.11n). These systems have different characteristics

with respect to latency, coverage, reliability, and data

rate. Although the latency of cellular communication

systems decreases with the evolution of these systems,

Wi-Fi systems provide a delay of only several

milliseconds in most situations. In contrast, the

coverage of Wi-Fi is significantly smaller when

compared with cellular communication owing to the

lower transmission power and higher frequency of

802.11p. The reliability of both the communication

classes depends on the environment and on the other

users within communication range. Typically, a

cellular system provides higher reliability than a Wi-

Fi based system; a cellular system also guarantees

quality of service (QoS) for the V2X applications

when compared with a Wi-Fi based system.

However, Wi-Fi systems are operating in an

unlicensed spectrum whereas the operators of

V2V

V2P

V2I

Pedestrian

Veh icle

Vehicle

Network

Moon, S., Bae, S., Chu, M., Lee, J., Kwon, S. and Hwang, I.

Adaptive Transmission Scheme for Vehicle Communication System.

DOI: 10.5220/0006409200930099

In Proceedings of the 7th International Joint Conference on Pervasive and Embedded Computing and Communication Systems (PECCS 2017), pages 93-99

ISBN: 978-989-758-266-0

cellular communications must pay for the

frequencies. The data rate is similar for both the

classes. Further, hybrid approaches, which combine

the advantages of cellular-based and Wi-Fi-based

communication systems, are suitable solutions for

efficient V2X communication. LTE has introduced a

device-to-device (D2D) communication link from

Release 12; therefore, cellular D2D can be used

instead of a Wi-Fi based system.

In this manuscript, we design a system-level

simulator (SLS) for LTE-based V2X and propose an

adaptive transmission scheme for vehicle

communication. The proposed scheme allocates the

resource randomly in the time and frequency domains

and transmits the message according to the probability

of transmission. The remainder of this manuscript is

organized as follows. Section 2 presents the design

and deployment of the V2X SLS. Section 3 describes

the details of the proposed adaptive transmission

scheme. Section 4 presents the performance analysis

of the proposed scheme based on simulations. Section

5 states the conclusion of the study.

2 DEPLOYMENT OF V2X

SYSTEM LEVEL SIMULATOR

In this section, we describe the V2X system

structure. Figure 2 shows the block diagram of the

V2X SLS. The V2X system consists of evaluation

scenario, user equipment (UE) drop and mobility

model, evolved Node B (eNB) and road side unit

(RSU) deployment, a channel model and traffic

model. In addition, we analyze the performance by

using the packet reception ratio (PRR).

Figure 2: Block diagram of V2X system-level simulator.

2.1 Evaluation Scenarios

We define two vehicle UE drop scenarios: Urban

scenario and Freeway scenario. The UE drop model

and mobility model are described in Section 2.2.

Further, the channel model for each scenario is

described in Section 2.4.

Macro eNB may or may not be deployed in the

evaluation. If it is deployed, the assumptions in

Section 2.3 should be used. If it is not deployed, a

simple wrap around can be used.

2.2 UE Drop and Mobility Model

Vehicle UEs are dropped on the roads according to

the spatial Poisson process. The vehicle density is

determined by the vehicle speed assumption, and the

vehicle location should be updated once every 100

ms in the simulation. In the urban scenario, a vehicle

changes its direction at the intersection as follows:

- Go straight with probability 0.5

- Turn left with probability 0.25

- Turn right with probability 0.25

Figures 3 and 4 illustrate the road configuration

for the two scenarios.

Figure 3: Road configuration for urban scenario.

Figure 4: Road configuration for urban scenario.

2.3 eNB and RSU Deployment

If macro eNBs are deployed in the freeway scenario,

the eNBs are located along the freeway at a distance

35 m away with an ISD of 1732 m, as shown in

SPCS 2017 - International Conference on Signal Processing and Communication Systems

Figure 5. If macro eNBs are deployed in the urban

scenario, the inter-site distance (ISD) of the macro

eNB is 500 m, and the wrap around model is as

shown in Figure 6.

Figure 5: Wrap around model for urban scenario.

Figure 6: Wrap around model for urban scenario.

2.4 Channel Model

The assumptions for the channel between two

vehicle UEs are given in Table 1.

Table 1: Channel model parameters.

Parameter Freeway scenario Urban scenario

Pathloss

model

LOS in

WINNER+ B1

WINNER+B1

Manhattan grid layout

Shadowing

distribution

Log-normal Log-normal

Shadowing

standard

deviation

3 dB

3 dB for LOS and

4 dB for NLOS

Decorrelatio

n distance

25 m 10 m

Fast fading

NLOS in Section A.2.1.2.1.1 or

A.2.1.2.1.2 in 3GPP TR 36.843 with fixed

large-scale parameters during the

simulation.

2.5 Traffic Model

In the evaluation, we use two traffic models:

periodic traffic scenario and event-triggered traffic

scenario. The periodic traffic scenario is mandatory.

The event-triggered traffic scenario can be evaluated

optionally with or without periodic traffic. Every

vehicle in the simulation generates messages

according to the traffic model.

For periodic traffic, the working assumption for

the message size is that one 300-byte message is

followed by four 190-byte messages, and the time

instant for the 300-byte size message generation is

randomized among vehicles. The message size can

be ignored while calculating the performance metric.

For event-triggered traffic, the event arrival follows

a Poisson process with the arrival rate of X (based

on company choice) per second for each vehicle.

Once the event is triggered, six messages are

generated within a span of 100 ms. The working

assumption for the message size of event-trigger

traffic at L1 is 800 bytes.

2.6 Performance Metric

In the evaluation of the proposed schemes for V2V,

the PRR will be considered. For one Tx packet, the

PRR is calculated as X/Y, where Y is the number of

UE/vehicles that are located in the range (a, b) from

the Tx, and X is the number of UE/vehicles with

successful reception among Y. The Cumulative

Distribution Function (CDF) of PRR and the

following average PRRs are used in the evaluation:

CDF of PRR with a = 0, b = baseline of 320 m

for freeway scenario and 150 m for urban

scenario. Optionally, b = 50 m for urban

scenario with vehicle speed of 15 km/h.

- Average PRR, calculated as (X1+X2+X3

….+Xn)/(Y1+Y2+Y3…+Yn), where n denotes the

number of generated messages in simulation, a =

i×20 m, b = (i+1)×20 m, and i=0, 1, …, 25.

3 ADAPTIVE TRANSMISSION

SCHEME

In this section, we propose an adaptive transmission

scheme for vehicle communication. The proposed

scheme allocates the resource randomly and

transmits the message according to the probability of

transmission.

The resource is allocated randomly in the time

and frequency domains. The resource units are

Adaptive Transmission Scheme for Vehicle Communication System

defined as illustrated in Figure 7. N

represents the

number of total resource blocks (RBs). M

denotes

the number of allocated RBs. Therefore, the resource

is allocated with a subchannel unit that consists of

RBs in the frequency domain. In addition, M

denotes the number of subframes used for message

transmission with the periodicity of T

subframes.

Figure 7: Resource unit structure.

Figures 8 and 9 show an example of the resource

allocation structure for the periodic and event-

triggered scenarios, respectively. In this figures, we

set M

=10 with N

=50 (for 10 MHz bandwidth) in

the frequency domain. Thus, the random frequency

range is 0 to 4 (0–(floor(N

)-1)). In addition,

we set M

SF,300B

=3 and M

SF,190B

=2 with T

=100 ms in

the time domain. Thus, the random time range is 0 to

97 ms (0–(T

-M

SF,1

)).

Figure 8: Resource allocation for periodic traffic.

Figure 9: Resource allocation for event-triggered traffic.

In addition, Tx UE transmits the message with a

probability P

. Thus, the interference effect

decreases and the performance improve because Tx

UE does not transmit the message with a probability

(1-P

). If Tx UE does not transmit the message, we

calculate the PRR that satisfies 100%.

4 SIMULATION MODEL AND

PERFORMANCE ANALYSIS

4.1 Simulation Model and Simulation

Parameters

A system-level simulation is performed to evaluate

the performance of the proposed scheme. The

simulation follows the 3GPP evaluation methodolo-

gy. The simulation is based on the freeway case

scenario in and periodic message transmission.

Table 2 shows the general simulation parameters and

defines the simulated environment.

Table 2: Simulation parameters.

Parameter Assumption

Carrier frequency for

PC5-based V2V

6 GHz

Bandwidth 10/20 MHz

Number of carriers One carrier

Synchronization Frequency error ± 0.1 PPM.

Vehicle

para-

meters

In-band

emission

In-band emission model with

{W, X, Y, Z} = {3, 6, 3, 3}

for single cluster SC-FDMA.

Antenna height 1.5 m

Antenna pattern Omni 2D

Antenna gain 3 dBi

Maximum tx.

power

23 dBm

Number of

antennas

1 TX and 2 RX antennas

Noise figure 9 dB

Number of lanes 3 in each direction

Lane width 4 m

Simulation area size Freeway length >= 2000 m.

Vehicle density

2.5 s ൈ absolute vehicle

speed

Absolute vehicle speed 70 km/h, 140 km/h

ISD 1732 m

Pathloss model LOS in WINNER+ B1

Shadowing distribution Log-normal

Shadowing standard

deviation

3 dB for LOS and 4 dB for

NLOS

Decorrelation distance 25 m

Fast fading

NLOS in Section A.2.1.2.1.1

or A.2.1.2.1.2 in 3GPP TR

36.843 with fixed large-scale

parameters during the

simulation.

Traffic Model Periodic traffic

Message size

One 300-byte message

followed by four 190-byte

messages

SPCS 2017 - International Conference on Signal Processing and Communication Systems

Further, the time and frequency resource in the

simulation is defined according to the category and

condition, as shown in tables 3 and 4, respectively.

Table 3: Category for simulation.

Category

Total

number of

RBs (N

)

Probability of

transmission

)

Number of

transmissions

(R)

1 100 1 4

2 100 1 2

3 50 1/2 4

4 50 1/2 2

Table 4: Condition for simulation.

300

Bytes

Number of RBs 10

Number of subframes

Code rate

(Modulation/I

TBS

)

0.3030

(QPSK/5)

190

Bytes

Number of RBs 10

Number of subframes

Code rate

(Modulation/I

TBS

)

0. 2879

(QPSK/5)

4.2 Simulation Results and

Performance Analysis

4.2.1 Resource Status

In this section, we analyze the resource status

according to the category in the simulation area, as

shown in Figure 6. The number of collision RBs,

unused RBs, and used RBs per subframe are listed in

Table 5 and Table 6 according to the vehicle speed,

category (CAT).

The number of allocated RBs (N

) is 100, and

the number of transmissions (R) is 4. Thus, the

number of collision RBs is the highest because the

number of used RBs is the highest. In the case of

category 2, N

is 100, and R is 2. Thus, we observe

that the number of collision RBs is lower than that in

category 1 owing to the decrease in the number of

used RBs that use a reduced number of

transmissions. In the case of categories 3 and 4, the

number of collision RBs decreases because the

probability of collision increases when the number

of allocated RBs is reduced to 50; however,

categories 3 and 4 do not transmit with a probability

1/2. In addition, the value of R for category 3 and 4

is 4 and 2, respectively. Thus, the number of

collision RBs decreases as the number of

transmissions decreases.

Table 5: Resource status: velocity 70km/h.

CAT Collision RBs Unused RBs Used RBs

1 67.4 12.1 87.9

2 37.9 30.4 69.6

3 31.6 8.3 41.7

4 19.6 13.3 36.7

Table 6: Resource status: velocity 140km/h.

CAT Collision RBs Unused RBs Used RBs

36.3 32.6 67.4

17.4 51.5 48.5

20.8 14.1 35.9

9.3 24.1 25.9

4.2.2 PRR

The CDF of PRR and the average PRR are used in

the evaluation. Figures 10 and 11 show the CDF of

PRR for vehicle speeds of 70 km/h and 140 km/h,

respectively. Figure 12 and Table 7 show the

average PRR for a vehicle speed of 70 km/h. Figure

13 and Table 8 show the average PRR for a vehicle

speed of 140 km/h.

Figure 10: CDF of PRR: velocity 70km/h.

0 0.2 0.4 0.6 0.8 1

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Packet Reception Ratio (PRR)

CDF

Periodic, Freeway 70km/h, Condition 1

Category 1

Category 2

Category 3

Category 4

Adaptive Transmission Scheme for Vehicle Communication System

Figure 11: CDF of PRR: velocity 140km/h.

Figure 12: Average PRR: velocity 70km/h.

Table 7: Average PRR: velocity 70km/h.

Range

(m)

CAT 1 CAT 2 CAT 3 CAT 4

20~40 0.9778 0.9770 0.9941 0.9948

60~80 0.9673 0.9682 0.9912 0.9928

100~120 0.9538 0.9576 0.9877 0.9902

140~160 0.9365 0.9443 0.9830 0.9870

180~200 0.9177 0.9291 0.9774 0.9829

220~240 0.8973 0.9111 0.9715 0.9781

260~280 0.8754 0.8920 0.9648 0.9729

300~320 0.8530 0.8700 0.9570 0.9666

340~360 0.8294 0.8482 0.9491 0.9596

380~400 0.8054 0.8245 0.9409 0.9520

420~440 0.7830 0.8013 0.9325 0.9441

460~500 0.7593 0.7787 0.9238 0.9359

500~520 0.7366 0.7566 0.9148 0.9273

Figure 13: Average PRR: velocity 140km/h.

Table 8: Average PRR: velocity 140km/h.

Range

(m)

CAT 1 CAT 2 CAT 3 CAT 4

20~40 0.9895 0.9863 0.9975 0.9970

60~80 0.9822 0.9805 0.9958 0.9957

100~120 0.9736 0.9721 0.9938 0.9939

140~160 0.9617 0.9623 0.9911 0.9909

180~200 0.9472 0.9493 0.9876 0.9876

220~240 0.9298 0.9335 0.9833 0.9832

260~280 0.9102 0.9157 0.9776 0.9786

300~320 0.8904 0.8959 0.9722 0.9731

340~360 0.8686 0.8756 0.9658 0.9664

380~400 0.8464 0.8542 0.9585 0.9593

420~440 0.8257 0.8316 0.9506 0.9513

460~500 0.8029 0.8086 0.9419 0.9434

500~520 0.7797 0.7865 0.9336 0.9347

5 CONCLUSIONS

In this study, we designed an SLS for an LTE-based

V2X and proposed an adaptive transmission scheme

for vehicle communication. We allocated the

resource randomly in the time and frequency

domains and transmitted the message according to

the probability of transmission. The performance

analysis was based on the freeway scenario and

periodic message transmission. Simulation results

show that our proposed scheme can improve the

CDF of PRR and the average PRR.

In future work, we will consider the resource

allocation algorithm in order to improve the

reliability of the LTE-based V2X system.

0 0.2 0.4 0.6 0.8 1

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Packet Reception Ratio (PRR)

CDF

Periodic, Freeway 140km/h, Condition 1

Category 1

Category 2

Category 3

Category 4

0 100 200 300 400 50

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

Distance [m]

Average PRR

Per iodic, Freeway 70km/h, Condition 1

Category 4

Category 3

Category 2

Category 1

0 100 200 300 400 500

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

Distance [m]

Average PRR

reewa

140k

diti

Category 4

Category 3

Category 2

Category 1

SPCS 2017 - International Conference on Signal Processing and Communication Systems

ACKNOWLEDGEMENTS

This research was supported by the MSIP(Ministry

of Science, ICT and Future Planning), Korea, under

the ITRC(Information Technology Research Center)

support program (IITP-2016-R2718-16-0011)

supervised by the IITP(Institute for Information &

communications Technology Promotion). This

research was supported by Basic Science Research

Program through the National Research Foundation

of Korea(NRF) funded by the Ministry of

Education(NRF-2015R1D1A1A01059397). This

study was financially supported by Chonnam

National University(Grant number: 2016-2503).

REFERENCES

3GPP TR 22.885, Study on LTE Support for V2X

Services.

Md. Sazzad Hossen, et al. 3, 2014. Performance Analysis

of an OFDM-based, ICUFN.

Zheng Li, et al. 3, 2013. Tentpoles Scheme: a Data-Aided

Channel Estimation Mechanism for Achieving

Reliable Vehicle-to-Vehicle Communications, IEEE

Transactions on Wireless Communications

Wai Chen, 2015. Vehicular communications and

Networks, Elsevier

3GPP TR 36.885, Study on LTE-based V2X Services.

3GPP TR 36.843, Study on LTE Device to Device

Proximity Services.

Adaptive Transmission Scheme for Vehicle Communication System