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M.S. THESIS

Adaptive Transmission Power RatioControl of SA and Data in D2D-based

V2V Systems

직접통신기반의차량통신에서 SA와 Data의전송전력비율조절기법

BY

LIM BYEONG-HUI

FEBRUARY 2017

DEPARTMENT OF ELECTRICAL ENGINEERING ANDCOMPUTER SCIENCE

COLLEGE OF ENGINEERINGSEOUL NATIONAL UNIVERSITY

Abstract

Long-Term Evolution Vehicle-to-Everything (LTE V2X) communication has taken

many attentions as key enabling technologies for public safety in road environments.

LTE V2X systems are classified into two systems; cellular-based V2V systems and

Device-to-Device (D2D)-based systems. This paper focuses on D2D-based systems.

For the vehicle safety operation, all vehicles should transmit two types of packets. One

is called as the scheduling assignment (SA) which delivers essential information for

data reception, and the other is the data which includes dynamic information of the

transmitting vehicle such as position, velocity, acceleration, etc. Since the power dif-

ference of two types of packets critically affects the performance of message reception,

we propose the adaptive power control criterion in SA and data.

In this paper, we analyze the optimal power ratio of SA and data in limited total

transmit power. This problem is analyzed based on stochastic geometry and solved as

optimization problem which maximizes packet reception probability of vehicles. We

also validate our analysis with system level simulation results.

keywords: LTE V2X, D2D V2V, Power Ratio Control, Random Resource Selection,

Packet Reception Probability, Stochastic Geometry

student number: 2015-20979

i

Contents

Abstract i

Contents ii

List of Tables iii

List of Figures iv

1 Introduction 1

2 System Model 3

2.1 Channel Model and Road Environment . . . . . . . . . . . . . . . . . 3

2.2 Resource Structure and Operation for D2D-based V2V Systems . . . 4

3 Performance Analysis 6

3.1 Message Reception Probability for D2D-based V2V systems . . . . . 6

3.2 Optimization of the transmission power ratio of SA and data . . . . . 10

4 Numerical Results 12

5 Conclusion 19

Abstract (In Korean) 21

ii

List of Tables

2.1 SA and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

3.1 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

4.1 System Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

iii

List of Figures

2.1 Resource structure of D2D-based V2V systems . . . . . . . . . . . . 4

3.1 D2D-based V2V systems scenario . . . . . . . . . . . . . . . . . . . 7

4.1 Analysis for message reception probability in Test 1 . . . . . . . . . . . . 15

4.2 Simulation for message reception probability in Test 1 . . . . . . . . . . . 16

4.3 Analysis for message reception probability in Test 2 . . . . . . . . . . . . 17

4.4 Simulation for message reception probability in Test 2 . . . . . . . . . . . 18

iv

Chapter 1

Introduction

Vehicle-to-Everything (V2X) communication has been one of important issues for next

generation networks. It becomes essential future communication systems as a solution

to improve road public safety. Due to characteristics of road safety, the vehicular envi-

ronment brings about unique challenges and requirements. Importantly, each vehicle

broadcasts own message in order to advertise its current state. To develop suitable

systems, many organizations have researched and standardized V2X systems.

The two standard groups of communication area, Institute of Electrical and Elec-

tronics Engineers (IEEE) and Third Generation Partnership Project (3GPP), have de-

veloped for new V2X systems in recent years. The technology of IEEE for V2X sys-

tems is called Wireless Access in Vehicular Environment (WAVE). WAVE is based on

Wireless Local Area Network (WLAN) technology which has a random access man-

ner of Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA). Because of

characteristic of this manner, WAVE shows poor performance in high vehicle density

environment [1]. This results bring about appearance of V2V systems with support of

cellular networks, called as LTE V2X.

LTE V2X has two branches of communication systems. One is the cellular-based

V2V systems which are similar to existing cellular mobile communication systems.

The other is the D2D-based V2V systems which exploit D2D technologies for V2V

1

environment. From release 12 of 3GPP standardize group, D2D systems have been

evolved for two purposes which are D2D discovery and D2D communication. Un-

til now, D2D-based V2V systems are based on D2D communication standard. D2D

communication overview is detailed in [2].

D2D-based V2V systems have two types of packet for transmitting a vehicle mes-

sage which are called as SA and Data. SA carries essential information for data recep-

tion such as modulation coding scheme (MCS), timing advance and time-resource pat-

tern of transmission, etc. Data carries dynamic vehicle information such as location,

velocity, acceleration and path history, etc. Naturally, resource pool for D2D-based

V2V systems is divided into two parts for SA and data. And these two packets can

be transmitted in same sub-frame and different sub-frame. Between two options, the

power allocation problem occurs in same sub-frame transmission. Because of limited

total transmission power, the power ratio control problem of SA and data has been

issued recently in 3GPP standard [3].

To optimize the transmission power ratio of SA and data, in this paper, we analyze

a message reception probability as a performance metric, which represents the recep-

tion success probability of a SA and a data packet between a transmitter and a receiver.

Message reception probability can be formulated as a function of SA and data power

to be allocated. This formulation can be represented with homogeneous Poisson Point

Process (hPPP) of stochastic geometry. This mathematical tool has been exploited in

cellular mobile communication systems. The related work is detailed in [4]. Then we

solve the optimization problem which maximizes message reception probability with

variables of SA and data power.

The rest of this paper is organized as follows. Chapter 2 describes the detailed

system model of D2D-based V2V. We take a system model from 3GPP standard. In

Chapter 3, we represent the analysis using the stochastic geometry. System level sim-

ulation results are given in Chapter 4. We finally conclude our paper in Chapter 5.

2

Chapter 2

System Model

In this chapter, we present the system model for D2D-based V2V systems. First, we

shows the spatial model for vehicle location. Channel model and fading are also con-

sidered. Second, we present the resource structure and general operation of D2D-based

V2V systems. In particular, the resource selection process for vehicular messages

would be mentioned.

2.1 Channel Model and Road Environment

The signal propagation model assumes the Rayleigh multi-path fading with exponen-

tial power fading h. We develop a mathematical analysis for D2D-based V2V systems

in two-dimensional space model. In this model, vehicles are described as points for

analyzing with point process of stochastic geometry theory. Two-dimensional space

model is possible to analyze mathematically and it is close to 3GPP standard vehi-

cle highway model. The detailed 3GPP standard vehicle environment models are de-

scribed in [5]. However, it is not similar to urban grid model because proposed model

can’t reflect various urban environment. For mathematical formulation, we choose the

simple two-dimensional space model.

3

2.2 Resource Structure and Operation for D2D-based V2V

Systems

Figure 2.1: Resource structure of D2D-based V2V systems

General explanation for D2D-based V2V systems are introduced in [5]. Recently,

A new arrangement of SA and data resources has been agreed. The arrangement is

illustrated in Figure 2.1. that is brought in [6]. In this figure, the SA of vehicle No.

0 is transmitted by Physical Sidelink Control Channel 0 (PSCCH0) and the data of

vehicle No. 0 is transmitted by DATA0. In the same way, the SA of vehicle No. 1

is transmitted by PSCCH1 and the data of vehicle No. 1 is transmitted by DATA1.

For satisfying the latency requirement of 3GPP standard, one resource pool covers

100ms time resources. As stated above, resource pool is divided into SA and data

pool. We assume that SA and data are FDMed like figure 2.1. Also, SA and data are

transmitted in resource block (RB) units and have different transmission manner. Table

2.1 shows the difference between SA and data. This parameters are brought from the

3GPP standard. Because of the difference of transmission manner, Signal to Noise

Interference Ratio (SINR) thresholds are different for SA and data. Thus, the power

ratio control is needed for reception balance between SA and data.

For transmission of SA and data, there are two options; random resource selection

4

Table 2.1: SA and DataParameters SA Data

Size 30 ∼ 60 bits (1RB pair) 300 bytes (15 ∼ 50 RB pairs)

Coding QPSK (Convolution coding) QPSK (Turbo coding)

Code rate 1/10∼1/5 1/6∼1/2

and sensing-based resource selection. Former is resource selection randomly each of

SA and data. Latter is the selection scheme that vehicles observe and sense the power

level of former SA and data. Then vehicles choose the lowest power level resource

based on formal power level information. This option is described in [7]. Between

two options, we assume random resource selection. In addition, SA and data have a

one-to-one mapping in same sub-frame transmission..

5

Chapter 3

Performance Analysis

Performance analysis is processed by two steps. First, we derive the mathematical

form of message reception probability in D2D-based V2V systems. Message recep-

tion probability is defined as the probability that SA and data from a transmitter are

successfully delivered to a receiver. It is a function of the distance between a trans-

mitter and a receiver, vehicle density, SA and data power, etc. Second, we optimize

this function with variables of SA and data power and constraint of total transmission

power. Because of its convexity, the optimization problem is analytically solved and

we can find the optimal solution.

3.1 Message Reception Probability for D2D-based V2V sys-

tems

To analyze a message reception probability, we use stochastic geometry. We assume

that vehicles in two-dimensional space model follow hPPP with vehicle density λ.

First, we analyze the probability that a vehicle successfully receive a message from

a transmit vehicle. Figure 3.1 represents the scenario in detail. The notations for two-

dimensional space model is shown by table 3.1. In figure 3.1, xi is a transmitter using

the PSCCH0 for SA transmission. xj is a receiver which observes the PSCCH0 used

6

Figure 3.1: D2D-based V2V systems scenario

by xi. If there is another vehicle using the PSCCH0, then the signal interferes to xj as

a collision. We assume that the collisions are only considered as a signal interference.

The criterion of successful reception is decided by SINR threshold. As mentioned

above, the SINR thresholds of SA and data are different.

Table 3.1: NotationsNotation Definition

λ Vehicle density in homogeneous poisson point process

Nt The number of sub-frames in a transmission period

NSAf , Ndata

f The number of SA, data resource blocks in frequency axis

ISA, Idata Interference caused by resource collision in SA, data

G, α Path-loss gain, exponent

ThSA, Thdata SINR threshold for SA, data reception

Pt, PSA, Pdata Total, SA, data transmission power

7

The message reception probability of SA is formulated as

Prm(PSA, Pdata) = PrSA(PSA) · Prdata(Pdata) (3.1)

To analyze the message reception probability, we analyze the packet reception proba-

bility of SA first.

PrSA(PSA) =Nt − 1

Nt· Pr(SINRSA > ThSA) (3.2)

The first term of the packet reception probability of SA is formulated as half-duplex

effect. This means that xj can successfully receive message of xi when it uses RB in

different time slot. This term is only considered in the packet reception probability of

SA because we assume that SA and data are one-to-one mapping in same sub-frame.

We represent this term as Phde for simple description. Then, the second term related

to SINR of SA in average packet reception probability can be denoted as

Pr(SINRSA > ThSA) = EISA,h

[Pr

(PSAhGr

−α

NSAf σ2 + ISA

> ThSA

)](3.3)

PSA is the SA transmission power of each vehicle, and ISA is the interference to the

receiver xj in SA pool. r is the distance between the transmitter xi and receiver xj .

At the receiver, the signal is also effected by Additive White Gaussian Noise (AWGN)

with variance σ2. ISA can be expressed as

ISA =∑x∈Φc

PSAhxGr−αx (3.4)

rx is the distance between collision vehicles xwith xi and the receiver xj . Under hPPP,

the topology distribution of collision vehicles in the space are represented by Φc. We

can expand the equation (3.3) as follows.

EISA,h

[Pr

(h > ThSA ·

NSAf σ2 + ISA

PSAGr−α

)](3.5)

Because h is represent a Rayleigh fading as fH(h) = e−h, we can average out h as

follows.

EISA

[exp

(− ThSA ·

NSAf σ2 + ISA

PSAGr−α

)](3.6)

= exp(−sNSAf σ2) · EISA [exp(−sISA)] (3.7)

8

where s = −ThSArα

PSAGfor simple description. We can expand the term related to ISA as

follows.

EISA [exp(−sISA)] = EΦc,hx

[exp

(− s

∑x∈Φc

PSAhxGr−αx

)](3.8)

= EΦc,hx

[ ∏x∈Φc

exp

(− sPSAhxGr−αx

)](3.9)

= EΦc

[ ∏x∈Φc

Ehx

[exp

(− sPSAhxGr−αx

)]](3.10)

= EΦc

[ ∏x∈Φc

1

1 + ThSArαr−αx

](3.11)

When we let f(x) = 11+ThSArαr

−αx

, then we can use definition of Probability Gener-

ating Functional (PGFL) to (3.11) because f(x) : ∀x ∈ R2 → [0, 1].

EΦc

[ ∏x∈Φc

f(x)

]= exp

(− λp

∫R2

(1− f(x))dx

)(3.12)

= exp

(− 2πλp

∫ ∞0

rx

1 + Th−1SAr

−αrαxdrx

)(3.13)

= exp

(− λpπr2Th

2/αSA

2π

αcsc(2π

α

))(3.14)

p is the probability of resource collision, and the value is 1NfNt

. (3.14) is deduced by

[8]. And we express λp as the collision density λc. Thus we can get a closed form of

the packet reception probability of SA transmission for D2D-based V2V systems as

follows.

PrSA = Phde · exp

(−ThSAN

SAf σ2

Gr−αPSA

)· exp

(− λcr2Th

2/αSA

2π2

αcsc(2π

α

))(3.15)

Because message reception probability for D2D-based V2V systems is determined by

successful transmission of both SA and data packet, we can derive the packet reception

probability of data in a similar way.

Prdata = exp

(−ThdataN

dataf σ2

Gr−αPdata

)· exp

(− λcr2Th

2/αdata

2π2

αcsc(2π

α

))(3.16)

Therefore, we can derive the message reception probability by substituting (3.15) and

(3.16) for (3.1).

9

3.2 Optimization of the transmission power ratio of SA and

data

As stated above, we optimize the message reception probability with variables, PSA

and Pdata. We can represent the optimization problem as follows.

maximizePSA,Pdata

Prm(PSA, Pdata)

subject to PSA + Pdata = Pt

0 ≤ PSA ≤ Pt

0 ≤ Pdata ≤ Pt

(3.17)

To solve the optimization problem, we arrange and the message reception probability

as follows.

Prm(PSA) = C · exp

(− p

PSA− q

Pt − PSA

)(3.18)

whereC = Phde·exp

(−λcr2Th

2/αSA

2π2

α csc(

2πα

))·exp

(−λcr2Th

2/αdata

2π2

α csc(

2πα

)),

p = ThSAG−1rαNSA

f σ2, q = ThdataG−1rαNdata

f σ2 for simple description.

For finding the optimal point of the objective function, we differentiate the function as

follows.

∂Prm

∂PSA= C ·

(p

P 2SA

− q

(Pt − PSA)2

)· exp

(− p

PSA− q

Pt − PSA

)= 0 (3.19)

Above the equation makes the PSA condition of a quadratic equation.

(p− q)P 2SA − 2PtPSA + pP 2

t = 0 (3.20)

By quadratic formulas

PSA =pPt ±

√(pPt)2 − (p− q)pP 2

t

p− q(3.21)

Generally, because Pdata is bigger than PSA and Ndataf is also bigger than NSA

f .

Therefore, we can assume that p ≤ q. From this condition, PSA is followed as

PSA =

√(pPt)2 − (p− q)pP 2

t − pPtq − p

(3.22)

10

By substituting p and q for (3.22),

P ∗SA =Pt

(√ThdataN

dataf ThSANSA

f − ThSANSAf

)ThdataN

dataf − ThSANSA

f

(3.23)

Therefore, we derive the optimal transmission power of SA, and can derive the optimal

transmission power of data through the equation P ∗data = Pt − P ∗SA.

11

Chapter 4

Numerical Results

In this chapter, the results of performance analysis are evaluated and compared with

system level simulation results. First we show the performance analysis and simulation

results which reflect the values of 3GPP standard parameter in Test 1. Then we propose

the environment which can show more performance gain in Test 2. All of the results

are evaluated by using MATLAB.

The detailed simulation parameters are described in Table 4.1.

We design the two-dimensional space model with 1500 (m) of one side, and only

observe the part of the space with 500 (m) of one side. It minimizes the side effect of

simulations with limitation of finite space. The accuracy of our analysis can be con-

firmed by comparing analysis Fig. 4.1 and simulation result Fig. 4.2. The difference

between analysis and simulation results are relatively small and show the same ten-

dency. Thus, we can verify the accuracy of our analysis.

Fig. 4.1 and Fig. 4.2 show the results for Test 1, which reflect the 3GPP standard pa-

rameters. Then, Fig. 4.3 and Fig. 4.4 show the results for Test 2, which reflect the 3GPP

standard parameters except SINR threshold for SA. From the results, we can find the

environment that our proposed scheme shows better performance. In other words, if

SINR threshold for SA increases, because the information of SA isn’t determined, the

proposed scheme will be more worthwhile.

12

Table 4.1: System Parameters

Parameter Test 1 Test 2

Space environment (m2) 1500×1500 1500×1500

Vehicle density λ (m−2) 2× 10−4 ∼ 4× 10−4 2× 10−4 ∼ 4× 10−4

Vehicle distribution Poisson point process Poisson point process

Fast fading Rayleigh fading Rayleigh fading

Total transmit power (dBm) 23 23

Noise power per RB (dBm) -121.44 -121.44

Noise figure (dB) 9 9

SINR threshold for SA (dB) 0 4

SINR threshold for data (dB) 4 4

Nt 100 100

NSAf 1 1

Ndataf 25 25

Cell radius (m) 500 500

13

With each of the result, we simulate in two vehicle density, 2 × 10−4(m−2) and

4 × 10−4(m−2). From the results, we can figure out the performance gain isn’t much

different. We can also find the performance gain grows remarkably as the target dis-

tance between a transmitter and a receiver increases. This result represents our pro-

posed scheme is more effective in high path-loss, severe noise and shadowing environ-

ment. Therefore, in real space, our proposed scheme will affect urban grid environment

effectively.

14

0 50 100 150 200 250 300

Distance between Tx & Rx (m)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Me

ssa

ge

re

ce

ptio

n p

rob

ab

ility

-4, analysis w/o optimization-4, analysis w/ optimization-4, analysis w/o optimization-4, analysis w/ optimization

Figure 4.1: Analysis for message reception probability in Test 1

15

0 50 100 150 200 250 300 350 400

Distance between Tx & Rx (m)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Me

ssa

ge

re

ce

ptio

n p

rob

ab

ility

-4, simulation w/o optimization-4, simulation w/ optimization-4, simulation w/o optimization-4, simulation w/ optimization

Figure 4.2: Simulation for message reception probability in Test 1

16

0 50 100 150 200 250 300

Distance between Tx & Rx (m)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Me

ssa

ge

re

ce

ptio

n p

rob

ab

ility

-4, analysis w/o optimization-4, analysis w/ optimization-4, analysis w/o optimization-4, analysis w/ optimization

Figure 4.3: Analysis for message reception probability in Test 2

17

0 50 100 150 200 250 300 350 400

Distance between Tx & Rx (m)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Me

ssa

ge

re

ce

ptio

n p

rob

ab

ility

-4, simulation w/o optimization-4, simulation w/ optimization-4, simulation w/o optimization-4, simulation w/ optimization

Figure 4.4: Simulation for message reception probability in Test 2

18

Chapter 5

Conclusion

In this paper, we derive the message reception probability of D2D-based V2V systems

mathematically. The main feature of the performance analysis is to show that we can

get a 3∼5% gain when the distance between a transmitter and a receiver is 150∼250m

in the 3GPP standard environment. Performance gain isn’t much large. But in the point

of a transmitter, there is no additional information for the optimal power transmission

ratio. To optimize the transmission power ratio, we just know the total power and

thresholds of SA and data. In addition, this proposed scheme has the potential to be

more worthwhile when channel environment are worse and the threshold of SA grows

up. The results of system level evaluation support our analysis.

In future work, we may include the other interference element to reflect real V2V

environment, e.g. in-band emission. This interference element affects relatively large

because there is no feedback scheme now in V2V systems. However, it has challenging

issue that makes the objective function non-convex therefore we may find the other

optimization method for solving this problem.

19

Bibliography

[1] Mir, Zeeshan Hameed, and Fethi Filali. “LTE and IEEE 802.11 p for vehicular

networking: a performance evaluation,” EURASIP Journal on Wireless Commu-

nications and Networking., pp. 1-15, Jan 2014

[2] J.Schlienz, A. Roessler “Device to Device Communication in LTE,” White Paper,

Rohde & Schwarz, 29 Sep 2015

[3] R1-164855, “Power Control for V2V,” Huawei, HiSilicon, May 2016

[4] 황영주, 성기원, 김성륜 “셀룰러 네트워크에서의 대규모 D2D 통신의 실현 가

능성 연구,” 한국통신학회 논문지, 제37권 B편, 제12호, 1091-1101쪽, 2012년

12월.

[5] 3GPP TR 36.885, “Study on LTE-based V2X Services,” ver.14.0.0, Jun 2016

[6] RP-161788, “V2V Work Item Completion,” Qualcomm Incorporated, LGE,

CATT, III, Panasonic, Huawei, HiSilicon, Kyocera, Ericsson, Vodafone, Sony,

Sep 2016

[7] R1-164510, “Discussion on Details of Sensing Operation for PC5 based V2V,”

LG Electronics, May 2016

[8] I. S. Gradshteyn and I. M. Ryzhik, “Tables of Integrals, Series, and Products,”

7th Ed., Academic Press, 2007

20

초록

최근 도로에서의 안전성을 높이기 위한 방안으로 차량 간의 통신이 해결책으

로각광을받아이를위해여러기술들이활발히논의되고있다.이중차량이공공

안정성을 위해 자신의 차량 정보를 직접 통신 기반으로 주위 차량들에게 메세지를

보내는 방법이 존재한다. 이때 차량은 두 가지 종류의 패킷을 보내게 되는데 이 두

가지패킷을같은시간자원에보내게될때이패킷들에대한전력할당문제가발

생하게 된다. 현재까지 이에 대한 성능 분석 및 메세지 수신율을 최대화하는 전력

할당최적화문제가연구되어있지않다.따라서본연구는직접통신기반으로차량

통신을 할 때에 메세지를 전달을 위해 보내는 두 가지의 패킷에 대한 전력 할당 문

제를 최적화하는 것을 수행한다. 마지막으로 성능 분석은 실제 시스템 레벨에서의

시뮬레이션결과와비교하여분석의정확도를확인한다.

주요어: 직접 통신 기반의 차량 통신, 전력 비율 조절, 패킷 수신 확률, 전력 비율

최적화,확률론적기하,성능분석

학번: 2015-20979

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