Analysis of multi ray propagation for dsrc communication on street with riss on sidewalls

Analysis of Multi Ray Propagation for DSRC Communication on Street with RISs on Sidewalls Analysis of Multi ray Propagation for DSRC Communication on Street with RISs on Sidewalls Guilu Wu1,2,3 1 Stat[.]

Analysis of Multi-ray Propagation for DSRC

Communication on Street with RISs on Sidewalls

Guilu Wu1,2,3

1 State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China

2Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education,

Chongqing University of Posts and Telecommunications, Chongqing, China

3 School of Internet of Things Engineering, Jiangnan University, Wuxi, China

{wugl}@jiangnan.edu.cn

Abstract—Each element in the Reconfigurable Intelligent

Sur-faces (RISs) can independently change the phase (or/and)

ampli-tude of the incident signal, so the RISs can be used to enable users

to better receive the signal transmitted by the transmitter In this

paper, a Multi-ray propagation model for RISs-assisted dedicated

short-range communication (DSRC) system is presented and

analyzed on street The 3-D geometry and image techniques in

ray tracing are combined to build the proposed model This

method could enhance the ray-tracing algorithm in RISs-assisted

communication networks In addition, the envelope strength of

the received signal is analyzed and verified in our experiments by

regulating phase of RISs in two cases Different reflection times

on transmitted signal wave bring different envelope strength of

the received signal The simulation results show that different

reflection times and adjustment angles directly affect the received

signal strength

Index Terms—Multi-ray propagation, Signal strength, DSRC,

RIS, Mobile communication

I INTRODUCTION

Ray tracing technology has been widely used to analyze

electromagnetic (EM) wave propagation in wireless

commu-nication, especially for indoor environment or multi-obstacle

scenario The brute force ray tracing technology [1] and

the image technology [2] are the most widely used typical

ray tracing technologies The brute force ray tracing method

involves the rays which will or will not be transmitted to the

receiver And the image method is well adopted to analyze

radio propagation with low complexity geometry and low

number of reflections [3]

Many significant scenarios have a lot of reflections which

occur between two parallel planes, such as between the ceiling

and the floor in indoor communication, between two walls in a

street and so on Taking intelligent transportation system (ITS)

as an example, the Dedicated Short-Range Communication

(DSRC) can well support non-safe applications in the Internet

of vehicles Meanwhile, the data information is developing as

a speed of explosion in vehicular networks The millimeter

Wave (mmWave) communication technology can provide a

large bandwidth to ensure the transmission of big data under

the premise of transmission link reliability for vehicular

net-works [4] However, the mmWave communication has relative

large path loss, and the transmission distance is not far

The phenomenon of multiple reflections caused by obstacles

exacerbates this problems affected by materials of reflecting obstacles These restricts the applications of mmWave-based vehicular networks

Reconfigurable Intelligent Surfaces (RISs) have recently emerged as a promising technology that enable novel and ef-fective functionalities [5] And RISs comprised of tunable unit cells could mitigate against the path loss through regulating their reflection coefficients in manipulating electromagnetic waves Besides, the design process of an RIS make it easy

to deploy on the obstacle surface, and the production cost

is cheap Apparently, the significant multiple reflections often occur inside urban area between two parallel plane walls Due

to short-distance transmission of mmWave communication, the signal from transmitter can be transmitted to the receiver through multiple reflection, as shown in Fig 1 Hence, the traditional image technology is no longer suitable for solving complex ray tracing problems Combining with 3-D geometry technique, the shortcomings of the image technology in tracing

a complicated signal ray reflected between two parallel surface can be solved partly When the fixed reflection angle is small, the complex calculation process on 3-D geometry and image techniques has not been effectively improved This paper introduces RISs to deploy on the two parallel surface of wall

to reduce complex ray tracing The RISs technique is applied for enabling the expected reflection angle between two parallel plane surfaces

Our main contributions can be summarized as follows

• The RISs technology is introduced to ray tracing for solving complicated computation in multiple reflection process RISs are deployed in the two parallel surface of wall in urban scenario

• The effect of RISs technology on ray tracing is analyzed

on the strength of the received signal Two cases which include even and odd reflection numbers are discussed in the receiver, respectively

• Simulation results reveal that the performance of RISs-assisted ray tracing on combining 3-D geometry and im-age technologies is improved on mitigating transmission fading of a signal

The rest of this paper is organized as follows In Section II,

a series of related research works are elaborated on channel

propagation aspects In Section III, we consider a simple traffic

scenario and revisit multiple reflection problem with RISs

Section IV deals with the strength of received signal with even

and odd reflections with RISs-assisted communications In

Section V, the performance analysis as well as the theoretical

validation are discussed in our simulation results Finally, we

conclude the paper in Section VI

II RELATED WORKS

The evaluation of radio transmission is increasing on

wire-less communication in past decade And ray tracing techniques

have been widely proposed to focus on radio propagation in

wireless networks, especially indoor environments [6], [7]

Both the image method and the “brute force” method are

discussed adequately in different two-dimensional (2-D) and

three-dimensional (3-D) ray-tracing radio propagation

envi-ronments Specifically, the “brute force” method considers

the transmitted rays that will or will not reach the receiver

In this process, a bundle of data is required to reach ray

tracing The image method is applied for reflections which

both plane surfaces are non-parallel and have low complexity

on radio propagation with geometries Although the 3-D

geometry technology can partially overcome the shortcomings

of the image method on reducing computational complexity

for reflections between two parallel plane surfaces, it is only

suitable for a few of multiple reflected rays Two methods

bring high complexity in data processing [8], [9]

Apparently, the ray-tracing technology is closely related to

the environment in which it is applied Reshaping the natural

environment has opened up a new world for researchers to

study this issue Enabling technologies such as intelligent

device, smart network infrastructure, embedded systems will

create new interfaces, new services, new products by creating

smart environments and smart spaces with improvement of

tra-ditional applications [10], [11] Meanwhile, the new network

architecture and radio access interface can be introduced to

improve the performance of networks while the complexity

increases without significantly in data processing A promising

reflective wireless technology is required to reshape wireless

environments in a smart way [12]

RISs can be implemented on many novel applications

and the corresponding performance metrics, such as capacity

analysis, spectral resource optimizations, channel estimation,

reliability analysis, are studied in wireless networks [13],

[14] Among them, the analysis of channel transmission has

always been a challenging task A recent study, in [15] where

the far-field path-loss model is derived using physical optics

techniques [16] develops free-space path loss models for

different RISs-assisted communication scenarios by analyzing

the physics and electromagnetic nature of RISs These works

developed accurate models to depict channel characteristics

for RISs-assisted wireless communication However, complex

analysis methods are difficult to widely promote and

ap-ply [17] proposes a simple but sufficiently accurate model

to analyze RISassisted wireless networks by leveraging

s-calar theory and the Huygens-Fresnel principle And a simple

physical model for RISs-enabled transmission model based on physical parameters is presented in [18]

But scatter incident signals and single reflection scenario are lack of consideration for practical application scenarios To the best of our knowledge, a few researches focus on the practical application scenario for RISs-assisted communication, such

as vehicular networks [19], [20], [21] Hence, the jointed framework of RISs-assisted DSRC system has drawn signif-icant attentions due to its superior capability in ITS In this paper, we propose a multi-ray propagation model for DSRC communication on street with RISs on sidewalls in mmWave environment In addition, the analysis of the proposed model which adopts 3-D geometry and image techniques is provided

to display the process of tracing multiple reflection signal rays from the sending vehicle to the receiving vehicle For different reflection times, the reflection paths are different Furthermore, the received signal strength is also affected in this case

III SYSTEM MODEL

In this section, a specific application with RISs-assisted communication system is introduced in ITS To get the trans-mission distance between the transmitter and the receiver after multiple reflection, the equivalent transmission distance representation method is given out by 3-D geometry and image technique Furthermore, the received signal strength

at the receiver is analyzed specifically for different cases

At a traffic intersection with tall buildings, the millimeter wave communication happens between the vehicle S and the vehicle D The vehicle D is waiting on left side of the intersection as of traffic light The vehicle S is driving slowly along the street from west to east while it transmits a signal

to the waiting vehicle D adopting to DSRC protocol The connectivity of wireless networks is modeled as a quasi-static wireless channel In this process, the limited communication capabilities achieve liberation with the help of RISs, which are deployed on the wall facade, as shown in Fig 1 This scenario can be modelled in a block form of parallel and perpendicular planes in a 3-D coordinate system The wall is completely covered by multiple large RISs The structure in a traffic intersection of communication environment is modeled

as plane surface

The EM wave from the vehicle S arrive at the vehicle D through multiple reflections on RISs In addition, Fig 2 also gives out the corresponding geometry legend The coordinate origin is at the dark spot symbol (•) Then, the location of the vehicle S and the vehicle D are specified as a point in a 3-D coordinate system, denoted as (xS, yS, zS) and (xD, yD, zD), respectively Hence, the distance taken by the line of sight path

is given by

DLOS=p(xS− xD)2+ (yS− yD)2+ (zS− zD)2 (1) where xS ∈ (−∞, −L/2], yS ∈ [0, L], xD ∈ [−L/2, L/2] and yD= L Besides, we assume that the antenna of vehicles have same height and level, zS= zD Specially, the height of

RIS

S

D

T M

Fig 1 Model of DSRC communication on street with RISs.

the transmitter and the receiver are same and can be ignored

in this case

In DSRC environment, a signal ray at the receiving terminal

D is multiple reflection signals by RISs Referring to Fig 1,

the multiple reflections between two parallel walls are happen

to relay and reflection a transmitted signal According to Fig 2

the transmission distance by a series of reflection signals is

expressed as

Dref = l1+ l2+ · · · + ln+1 (2) where, li is path length for each segment, l1, l2, · · · , ln+1,

respectively The number of n reflections are determined by

deployment in practice Generally, n should not be too large

as of transmission path loss

From Fig 1 and Fig 2, a signal undergoes n reflections

between two parallel walls In order to facilitate the analysis

and design, we assume all angles, ψ0 = ψ1 = ψ2 = · · · =

ψn−1 = ψe/o, are equal through adjusting the corresponding

phase of an RIS on the wall It is noted that the geometry

angle of forward reflection wave satisfies ψe/o∈ [0, π/2] and

cos(ψe/o) ∈ [0, 1] Based on 3-D geometry technique [3],

the multiple reflection paths with eq (2) can be rewritten as,

specifically,

(

Dref (even)= (n+1)yD−yScos(ψ

e )

Dref (odd)= nyD +yS

cos(ψo)

(3)

tan−1(ψe) =(n+1)yxS−xD

D −yS tan−1(ψo) = xS −xD

nyD+yS

(4)

where, n is the number of reflections (even order or odd order)

According to the above analysis and geometric

relation-ship, the possible values of Dref (even) and Dref (odd) are

{Dref (even), Dref (odd)} ∈ [nL, +∞)

IV ANALYSIS OF THE RECEIVED SIGNAL STRENGTH

The received strength of the line of sight (LOS) signal can

be expressed as [22]

ELOS= ETΓe

−j[2πDLOSλ ]

DLOS

where, ELOS is the signal strength at the receiver, and ET

is the signal strength of the transmitter, and Γ is transmission coefficient of obstacle, and DLOS represents the distance of LOS between transmitter and receiver, and λ is wavelength of transmission signal

When the signal is transmitted on channel through multiple reflection, the multiple reflection transmission is equivalent to one LOS transmission by 3-D geometry and image techniques for reducing computational complexity Hence, the received strength of a multiple reflected signal is given as

ER= ETΓe

−j[2πDrefλ ]

Dref

where the sum of path length, Pn+1

i=1 li, can be obtained by

Dref in eq (3)

We assume that the transmission signal which has unit

pow-er is an unmodulated carripow-er signal u(t), u(t) = A cos(2πfct+ Φ), where fcis the carrier frequency and Φ is the initial phase

We adopt the low-pass complex equivalent of this signal by dropping the carrier term to represent as, u(t) = A exp(jΦ)

As of the Doppler spectrum with the moving vehicle S, we consider all reflected signals have unit constant amplitudes, such as A = 1, for rapidly varying phase terms

We assume the time-varying reflection coefficient of an RIS

is β(t) = ejψ(t), which has unit gain amplitude and the phase term is constantly changing Then, the n reflection coefficients

at each reflection point, R1, R2, · · · , Rn, on the parallel street constitute a set of vectors [β1, β2, · · · , βn] According to eq (6), the received strength of this signal can be rewritten as

ER= e

jΦQn i=1βie−j2π(

Pn+1 i=1 li) λ

Pn+1 i=1 li

In Fig 1 and Fig 2, the transmission signal is transmit-ted from the vehicle S to the vehicle D through multiple reflections on RISs Submitting β(t) into eq (7), the received strength of this signal can be rewritten as

ER=e

jΦej(ψ0+ψ1+···+ψn)e−j2π(l1+l2+···+ln+1)λ

l1+ l2+ · · · + ln+1

=e j(Φ+$−2πDrefλ )

Dref

(8)

Let us suppose $ = ψ0+ ψ1+ · · · + ψn It is well known that, if

Φ + $ = 2π

λDref+ 2kπ, k ∈ Z, (9) then, the received complex envelop of signal has maximum strength for the minimum Dref Otherwise, if

Φ + $ =2π

λ Dref + (2k + 1)π, k ∈ Z, (10) the received complex envelop of signal has minimum strength for the maximum Dref Against this background, the different total phase values $ can be achieved through controlling each phase value ψi of different RISs for satisfying eq.(9) and eq.(10)

RIS

S

D

L L/2

R 1

R 3

Left

Right

N

1

j

2

n

j

v

1

l

2

l

3

l

n

+ 0

j

Fig 2 Geometry digram of multi-ray propagation for DSRC communication with RISs.

In other words, the maximum strength condition on the

received complex envelop of signal is given as

$ =2π

λ · nL − Φ + 2kπ, k ∈ Z, when Dref = nL

(11)

and otherwise, the minimum strength condition on the received

complex envelop of signal is also given as

In this case, the controller (i.e., FPGA) adopts a centralized

method to conveniently realize the appropriate phases on RISs

in real-time [5], [23]

V SIMULATIONRESULTS

In this section, the propagation model has been developed

in the Matlab program The simulation scenario is build in

Fig 1 All walls is combination of bricks and RISs The signal

is transmitted from the transmitter to the receiver through

multiple reflections

A Parameters Setting

Having derived the received complex envelop of the

mul-tiple reflection signal in the vehicle D, we now consider

the qualitative dependence of the number of reflection on

the proposed model parameters Referring to eq (3), eq

(7) and eq (8), we focus on the received complex envelop

with different number of reflections for RISs-assisted DSRC

networks We assume that the width of the street is L = 8

m The vehicle S moves at a slowly speed v from the initial

position xS = −1000 m The mmWave operating frequency is

30 GHz For ease of presentation, the amplitude of reflection

coefficient with RISs is one unit and the transmission signal

is an unmodulated radio frequency carrier signal

B Results Analysis

In order to capture the effects of the complex envelope

strength in the received signal of RISs-assisted

communica-tion, we regulate the different number of reflections (Even

and Odd) on sidewalls Fig 3 and Fig 4 display the envelope

strength of the received signal with different phases, $, of

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

10-5 The Even Number of Reflection, N

n=6 n=8 n=10

Fig 3 The envelope strength of the received signal for the vehicle D

at different phases of RISs when a transmission signal from the vehicle S undergoes even number of reflections.

RISs As a whole, the maximum and minimum envelope strength of the received signal are corresponding to $ = 0o and $ = 90o, respectively When 0o < $ < 90o, the transmission signal from the vehicle S bounces between the parrel sidewalls many times before arriving to the vehicle D Two extremes for $ = 0oor $ = 90obasically represent that

a transmission signal travels in a line of sight or in blocking state, respectively

Specifically, Fig 3 shows the envelope strength of the received signal by different even number of reflections from

0o to 90oon phases of RISs and the results undergo a steady decline to 0 At even number of reflections, the envelope strength of the received signal decreases as the number of reflections increase for the same phase of RISs The more number of reflections mean the longer transmission distance, the signal dropping off sharply with distance When the phase

of RIS is 90o, there is no signal arrive at the receiver Hence, the envelope strength of the received signal is zero

Similarly, the similar characteristics are displayed in Fig 4 when the number of reflections are odd The envelope strength

0 10 20 30 40 50 60 70 80 90

0

0.5

1

1.5

2

2.5

3

3.5

10-4 The Odd Number of Reflection, N

n=7 n=9 n=11

Fig 4 The envelope strength of the received signal for the vehicle D

at different phases of RISs when a transmission signal from the vehicle S

undergoes odd number of reflections.

of the received signal will decreases sharply when the received

signals undergo briefly and smoothly in the initial stage

from 0o on phases of RISs However, it’s interesting that

the envelope strength of the received signal from the odd

number of reflections (n + 1) have significant improvements

for comparing with even number of reflections (n) signal

According to 3-D geometry and image methods in ray tracing,

the extra distance is the reason for this result

VI CONCLUSION

The proposed jointed propagation model for RISs-assisted

DSRC system on sidewalls based on 3-D geometry method and

image method offers a novel solution to the study of channel

propagation characteristics In multiple reflections between

two parallel plane, the 3-D geometry method improves the

ray tracing technique for measurement of complexity and

the image technique mirrors the signal source at a particular

face Introduction and design of RISs further promote the

performance of DSRC system

The multi-ray propagation for RISs-assisted DSRC

com-munication can be analyzed deeply on the different entry and

exit angle of signal for each RIS deployment position in future

work

ACKNOWLEDGMENT

The authors wishes to thank the anonymous reviewers for

their helpful comments that have significantly improved the

quality of the presentation This work has been supported

in part by the Jiangsu Planned Projects for Post-doctoral

Research Funds project under grant 2020Z113, the Open

Foundation of Key Laboratory of Industrial Internet of Things

& Networked Control, Ministry of Education under grant

2019FF09

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