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1

On the Performance of RIS-Assisted Dual-Hop

UAV Communication Systems

Liang Yang, Fanxu Meng, Jiayi Zhang, Mazen O. Hasna, and Marco Di Renzo

Abstract—In this paper, to further improve the coverage and

performance of unmanned aerial vehicle (UAV) communication

systems, we propose a reconﬁgurable intelligent surface (RIS)-

assisted UAV scheme where an RIS installed on a building is

used to reﬂect the signals transmitted from the ground source

to an UAV, and the UAV is deployed as a relay to forward

the decoded signals to the destination. To model the statistical

distribution of the RIS-assisted ground-to-air (G2A) links, we

develop a tight approximation for the probability density function

(PDF) of the instantaneous signal-to-noise ratio (SNR). Thanks

to this distribution, analytical expressions of outage probability,

average bit error rate (BER), and average capacity are derived.

Results show that the use of RISs can effectively improve the

coverage and reliability of UAV communication systems.

Index Terms—Bit error rate, capacity, outage probability, UAV,

RIS.

I. INTRODUCTION

Unmanned Aerial Vehicle (UAV) communication is playing

an increasingly important role in today’s wireless communica-

tion systems. Due to their small size and ﬂexibility, UAVs are

widely used for rapid networking in communication-disabled

areas. Also, UAVs can be used to establish ad hoc networks in

areas where signals from base stations are not always available,

such as mountains and deep desert areas.

Research on UAV communication systems is gaining more

and more attention. In [1], a UAV-aided relaying system

with energy harvesting capabilities was proposed, and the

outage probability as a function of different environmental

parameters was analyzed. In [2], the secrecy performance of

an air to air (A2A) communication system in the presence

Copyright (c) 2015 IEEE. Personal use of this material is permitted.

However, permission to use this material for any other purposes must be

obtained from the IEEE by sending a request to pubs-permissions@ieee.org.

Manuscript received Mar 4, 2020; revised May 18, 2020; accepted June

22, 2020. This work was in part supported by the National Natural Sci-

ence Foundation of China (NSFC) under Grant 61671160, the Department

of Education of Guangdong Province (No. 2016KZDXM050), the Hunan

Natural Science Foundation under Grant (No.2019JJ40043), the Science and

Technology Program of Guangzhou (No.201904010249), the Science and

Technology Program of Changsha (kq1907112), and the Open Fund of IPOC

(BUPT). The review of this paper was coordinated by Prof. Daniel Benevides

da Costa. (Corresponding author: Liang Yang)

L. Yang and F. X. Meng are with the College of Computer Science and

Electronic Engineering, Hunan University, Changsha 410082, China. (e-mail:

liangy@hnu.edu.cn).

Y. Zhang is with the School of Electronic and Information En-

gineering, Beijing Jiaotong University, Beijing 100044, China. (e-

mail:jiayizhang@bjtu.edu.cn).

M. O. Hasna is with the Department of Electrical Engineering, Qatar

University, Doha 2713, Qatar. (e-mail: hasna@qu.edu.qa).

M. Di Renzo is with the Paris-Saclay University (L2S-CNRS,

CentraleSupelec, University Paris Sud), Paris, France. (e-mail: mar-

co.direnzo@l2s.centralesupelec.fr).

of eavesdroppers was studied. Therein, it was shown that

that use of UAVs can enhance the average secrecy capacity.

In [3], the authors analyzed the physical layer security of a

UAV communication system in Rician fading channels and in

the presence of Poisson-distributed eavesdroppers. In [4], the

authors used the a UAV as a relay station and optimized its

trajectory to achieve the maximum throughput.

Reconﬁgurable intelligent surfaces (RISs) are man-made

surfaces of electromagnetic (EM) material that can be electron-

ically controlled via integrated electronics [5]. As an emerging

technology, RISs have shown great potential for communi-

cation applications, and are expected to become one of the

key technologies of future wireless networks. Recent results

show that RISs can effectively control the characteristics of an

incident signal, such as the phase, amplitude, and frequency,

without the need for complex decoding, encoding, and radio

frequency (RF) processing operations. In [6], the authors out-

lined current research activities and discussed the availability

of intelligent reconﬁgurable metasurfaces. In [7], the authors

gave a detailed overview and historical review of the latest

solutions for RISs, summarized the differences between RISs

and other technologies, and discussed open research problems.

In [8], the authors used an RIS to improve the achievable

rate of an UAV-enabled communication system and proposed

an optimization algorithm to maximize the average achievable

rate. In [9], the authors proposed a deep learning method to

conﬁgure the operation of an RIS in an indoor communication

environment. In [10], the authors studied RISs for application

to a downlink multi-antenna multiuser communication system.

In [11], the authors proposed a practical phase shift model

for RIS-aided wireless systems. In [12], the authors evaluated

the performance of RISs for application to dual-hop free-

space optical and radio frequency (FSO-RF) communication

systems. In [13], the authors studied the coverage and signal-

to-noise ratio (SNR) gain of RIS-assisted communication

systems.

The applications and performance of RISs in UAV-aided

communication systems has, however, not been well inves-

tigated in the literature. Motivated by this consideration, we

consider an application scenario in which an RIS is employed

to reﬂect the signals transmitted from a ground source to an

UAV, and the UAV is, in turn, employed for transmitting the

signal to the destination by using a decode-and-forward (DF)

protocol. It is worth noting that the considered system model

is different from the one analyzed in [8], in which an UAV is

used as an air base station. In our considered system model,

in fact, the UAV is used as a relaying station. Moreover, the

authors of [8] assume that the UAV-RIS link is affected only

2

S

RF

D

UAV

o

RIS

Fig. 1. Diagram of the considered RIS-assisted UAV relaying system.

by the path-loss and the RIS-to-ground link is subject to Rice

fading. However, the RIS-UAV channel may be subject to

channel scattering in addition to the line of sight (LoS) path. In

addition, multi-path components may exists on the ground-RIS

channels, e.g., due to the scattering induced by foliage. Thus,

we use the Rician and Rayleigh distributions to model the RIS-

UAV and ground-RIS channels, respectively. The proposed

model may ﬁnd application in low altitude UAVs, in which, to

further enhance the transmission reliability, the ground source

transmits signals to the RIS installed on a building, and the RIS

reﬂects the signals to the UAV through optimal beamforming

so as to maximize the received SNR at the UAV. In particular,

the main contributions of this work include the following: (i)

we propose a tight approximation for the statistical distribution

of the RIS-assisted ground-to-air (G2A) link; (ii) we introduce

an analytical framework for estimating the outage probability,

average bit-error rate (BER), and average capacity; and (iii)

we study the optimal altitude and position of the UAV for

performance optimization.

II. SY ST EM M OD EL

As shown in Fig. 1, consider an RIS-assisted UAV relaying

system that includes a source (S), an RIS installed on a

building, a UAV-aided relay station, and one destination (D).

Similar to most relaying systems, two time slots are needed

to complete the information transmission. In the ﬁrst stage,

S sends a signal to the UAV through an RIS-assisted G2A

link. In particular, the S ﬁrst sends the signals to the RIS, and

then the RIS, conﬁgured with optimal phase shifts, reﬂects

the signals to the UAV. In the second stage, a DF relaying

protocol is considered and it is assumed that the UAV can

successfully decode the received signals. Finally, the UAV

sends the decoded signals to D through an air-to-ground (A2G)

link.

A. RIS-Assisted G2A Link

In the ﬁrst time slot, S sends the signal to the UAV through

an RIS-assisted G2A link. Then, the received signal at the

UAV can be written as

y1="N

X

i=1

hiejϕigi#x1+n1,(1)

where x1is the transmitted signal, ϕiis the phase shift applied

by the ith reﬂecting element of the RIS, hi=1

√LSR βie−ψi

and gi=1

√LRU ie−φiare the channel gains, βiis a Rayleigh

random variable (RV) with mean √π/2and and variance

(4 −π)/4,iis a Ricain RV, LS R = 10 log 10(lα

SR ) + A

and LRU = 10 log 10(lα

RU ) + Aare the path loss, A is a

constant that depends on the signal frequency and transmission

environment, lSR and lRU are the distances of the S-RIS

and RIS-UAV links, respectively, αis the path loss exponent,

and n1∼ CN(0, N0)is the additive white Gaussian noise

(AWGN).

From [7], we know that the maximum SNR can be obtained

by setting ϕi=ψi+φi. Thus, the received signal can be

rewritten as

y1= N

X

i=1

βii!x1+n1.(2)

From (2), the maximum instantaneous SNR at the UAV can

be expressed as

γ1=PN

i=1 βii2Es1

N0L1

=R2Es1

N0L1

,(3)

where L1=LSR LRU , and Es1is the power of the signal.

By deﬁning χi=βii,Rturns out to be the sum of

Nindependent and identically distributed (i.i.d.) RVs χi,

i=1, ..., N . To the best of the authors’ knowledge, the exact

probability density function (PDF) of Rhas not been reported

in the literature. Therefore, for analytical tractability, we

use a mixture gamma distribution to approximate the Rician

distribution. From [14, Eqs.(1), (21)], the PDF of ican be

expressed in terms of a mixture gamma distribution as

fi(r) =

M

X

j=1

2ajr2bj−1e−cr2,(4)

where Mis the number of terms of the approximation, aj=

dj

PM

m=1 dmΓ(bm)c−bm,bj=j,c= 1 + K1,dj=Kj−1

1(1+K1)j

eK1[(j−1)!]2

are the parameters of the jth Gamma component, Γ(·)is the

gamma function, and K1is the Rician factor. Then, the PDF

of χican be calculated as

fχi(r) = Z∞

0

1

xfi(x)fβir

xdx, (5)

where fβi(x)is the PDF of βi. By using [15, Eq. (8.432.6)]

and (4), (5), the PDF of χican be expressed as

fχi(r)=

M

X

j=1

4ajrbjK1−bj2√cjr

cbj−1

2

=

M

X

j=1

ajΓ(bj)

cbjfj(r),(6)

where fj(r) = 4c

1+bj

2

Γ(bj)rbjK1−bj(2√cr)and Kv(·)is the

modiﬁed vorder Bessel function of the second kind [15, Eq.

(8.432)].

By comparing fj(r)with the PDF of a generalized-K(KG)

distribution [16], one can observe that the PDF of fj(r)is

a special case of the KGdistribution. Thus, the PDF of χi

is a mixture KGdistribution. In [16], it was stated that the

PDF of the sum of multiple KGrandom variables can be

3

well approximated by the PDF of √Wwith W=PN

j=1 χ2

i.

Therefore, the PDF of Rcan be formulated as

fR(r) = 4Ξkw+mw

Γ(kw)Γ(mw)rkw+mw−1Kkw−mw(2Ξr),(7)

where kw=−bw+√b2

w−4awcw

2awand mw=−bw−√b2

w−4awcw

2aw

are the the shaping parameters, Ξ = pkwmw/Ωw, and Ωw=

µR(2) is the mean power of R. Moreover, the parameters aw,

bw, and cwhave been deﬁned in [16] and their values are

related to the moment µR(n)of R, namely,

µR(n) =

n

X

n1=0

n1

X

n2=0

...

nN−2

X

nN−1=0 n

n1n1

n2...nN−2

nN−1

×µχ1(n−n1)µχ2(n1−n2)...µχN−1(nN−1),(8)

where µχi(n) = PM

j=1

ajΓ(1+n/2)Γ(bj+n/2)

c(bj+n/2) is the nth mo-

ment of χi. Notice that kwand mware real numbers. When

kwand mware conjugate complex numbers, kwand mware

set to the estimated modulus values of the conjugate complex

number.

From (3) and (7), the PDF of γ1can be readily given by

fγ1(γ)=

2Ξkw+mwγkw+mw

2−1Kkw−mw2ΞqγL1

¯γ1

Γ(kw)Γ(mw) (¯γ1/L1)kw+mw

2

,(9)

where ¯γ1=Es1

N0is the average SNR. As shown later, the

expression of the PDF in (7) provides tights estimates of the

system performance.

B. A2G Link

In the second time slot, the UAV sends the decoded signal

to D through an A2G link. Then, the received signal at D can

be expressed as

y2=hDx2+n2,(10)

where hD=1

√L2ηeξiis the channel gain of the A2G link and

n2∼ CN(0, N0)denotes the AWGN noise, and x2denotes

the encoded signal. Therefore, the instantaneous SNR at D can

be written as

γ2=|η|2Es2

N0L2

,(11)

where L2= 10 log 10(lα

2) + Ais the path loss, and Es2is the

transmit power of the UAV, l2=ph2+r2

2is the distance

between D and UAV, his the height of the UAV, and r2is the

horizontal distance between the UAV and D. From [17], the

PDF of γ2can be written as

fγ2(γ) =(1 + K2)e−K2L2

¯γ2

exp −(1 + K2)L2γ

¯γ2

×I0 2sK2(1 + K2)L2γ

¯γ2!,(12)

where ¯γ2=Es2

N0, and K2is the Rician fading factor.

The availability of a LoS links is usually determined by the

density of obstacles on the propagation path. Therefore, we

model the path loss exponent as [18] α(θ) = A1PLoS (θ)+B1,

where PLoS is the LoS probability, A1and B1are deter-

mined by the environment and the transmission frequency,

θ∈ {θ1, θ2}, and θ1and θ2represent the angles between the

UAV and S, D, respectively. Similarly, we model the Rician

factor as a function of θ. From [19], we have K(θ) = A2eB2θ,

where A2=k(0) and B2=2

πln(K(π

2)

K(0) ).

III. PERFORMANCE ANALYSIS

In this section, we analyze the outage probability, average

BER, and average capacity of the considered system model.

In addition, some asymptotic results are given in order to gain

some insights for system design.

A. Outage Probability Analysis

1) Exact Analysis: The reliability of a communication link

is usually assessed by the outage probability. Since we use a

DF relaying protocol at the UAV, a successful communication

requires that both links are uninterrupted. Thus, the outage

probability can be readily expressed as

Pout = Pr(min {γ1, γ2} ≤ γth )

=Pout1+Pout2−Pout1Pout2.(13)

By using [20, Eq. (07.34.21.0084.01)] together with (9), Pout1

can be written as

Pout1=1

Γ (kw) Γ (mw)G2,1

1,3Ξ2L1γth

¯γ1|1

kw,mw,0,(14)

where Gm,n

p,q [·]is the Meijer G-function.

Similarly, from (12), Pout2can be obtained as

Pout2= 1 −Q1p2K2,p2γth (1+K2)L2/¯γ2,(15)

where Qµ(·;·)is the Marcum Q-function with parameter µ.

2) Asymptotic Analysis: At high SNRs, the overall system

outage performance can be asymptotically expressed as

Pout →PA

out1+PA

out2.(16)

Using [20, Eq. (07.34.06.0006.01)] and [21, Eq.(43)], we have

PA

out1≈Γ(mm−km)

Γ(1 + km)Γ(mm)Ξ2L1γth

¯γ1km

,(17)

where mm= max(kw, mw), km= min(kw, mw).

From [22], the asymptotic outage probability over Rician

channels can be readily obtained as

PA

out2→(1 + K2)L2γth

eK2¯γ2

.(18)

From (17) and (18), we see that the achievable diversity

order of the considered system is Gd= 1 since kmis always

large than 1. Thus, for high SNRs, the outage probability

of the RIS-aided G2A link approaches to zero due to the

use of RISs. Then, the whole end-to-end system performance

mainly depends on the A2G link, which will be veriﬁed in the

numerical results section.

4

B. Average BER

1) Exact Analysis: For a dual-hop communication system

with DF relaying, the average BER can be written as [23]

PBER =Pe1+Pe2−2Pe1Pe2,(19)

where Pe1and Pe2are the average BERs of the ﬁrst and

second hop, respectively. For different binary modulation

schemes, a uniﬁed average BER expression is given by [24]

PBER =qp

2Γ(p)Z∞

0

exp(−qγ)γp−1F(γ)dγ, (20)

where the parameters p and q denote different modulation

schemes, such as p= 0.5 and q= 1 for binary phase shift

keying (BPSK), p= 0.5 and q= 0.5 for binary frequency shift

keying (BFSK), and p= 1 and q= 1 for differential phase shift

keying (DPSK). In this work, we consider DPSK modulation.

From [20, Eq. (07.34.21.0085.01)(07.34.21.0088.01)] and

(14), (20), Pe1can be formulated as

Pe1=1

2Γ(kw)Γ(mw)G2,2

2,3Ξ2L1

¯γ1|0,1

kw,mw,0.(21)

Similarly, using (15) and (20), and [25, Eq. (8)], we have

Pe2=1 + K2

2(1 + K2+¯γ2

L2)eK21F1 1; 1; 1 + K2

1 + K2+¯γ2

L2!,(22)

where 1F1(·)is the is the conﬂuent hypergeometric function

[15, Eq. (9.14.1)].

2) Asymptotic Analysis: Similar to (16), the BER at high

SNR can be asymptotically written as

PA

BER →PA

e1+PA

e2.(23)

Using [20, Eq. (07.34.06.0006.01)], we have

PA

e1≈Γ(mm−km)

2Γ(km)Ξ2L1

¯γ1km

.(24)

Again, from [22], the asymptotic BER over Rician channels

can be expressed as

PA

e2→L2(1 + K2)Γ(3

2)

√πeK2¯γ2

.(25)

As a double check, one can see that the diversity order Gdis

equal to 1.

C. Average Capacity

For a dual-hop DF system, the ergodic capacity can be

calculated as

C=Z∞

0

log2(1 + γ)fγ(γ)dγ, (26)

where fγ(γ)=fγ1(γ)+fγ2(γ)−fγ1(γ)Fγ2(γ)−fγ2(γ)Fγ1(γ),

Fγ1(γ)and Fγ2(γ)are the cumulative distribution functions

of γ1and γ2, respectively. Evaluating the integrals in (26) in-

volves some complex Fox H-functions. Thus, we only present

a very tight upper bound analysis. From [26, Eq. (23)], by

applying the Jensen’s inequality, a tight upper bound of the

average capacity can be expressed as

C≤1

2log2(1 + E(γ)),(27)

0 2 4 6 8 10

h[km]

10-3

10-2

10-1

100

Outage Probability

Analysis

Simulation

Without RIS

With RIS

N= 3

r= 2km, 1.5km

r= 2km, 1.5km

Fig. 2. Outage probability versus hfor a dual-hop UAV system with and

without RISs for different coverage radius.

where

E(γ)=

M

X

j=1

aj¯γ2

cbj+1Γ(kw)Γ(mw)L2

G3,2

3,4Ξ2L1¯γ2

cL2¯γ1|0,−bj,1

kw,mw,0,1,

(28)

For ¯γ1= ¯γ2= ¯γ→ ∞, we have

C→1

2log2

M

X

j=1

Wj¯γ

=1

2log2(¯γ) + 1

2log2

M

X

j=1

Wj

,

(29)

where Wj=aj

cbj+1Γ(kw)Γ(mw)L2G3,2

3,4hΞ2L1

cL2|0,−bj,1

kw,mw,0,1i.

IV. NUMERICAL RESULTS

In this section, we present some numerical results to verify

our analysis. The parameters setup used in the ﬁgures is A=

1,K(0) = 5dB, K(π/2) = 15dB, A1=−1.5,B1= 3.5,

p= 1,q= 1, and ρ=rSU

rSU +rU D represents the horizontal

distance ratio of the ﬁrst hop to the sum of two hops. As

for the system without an RIS, we assume that the source

equipped with one antenna communicates directly with the

UAV over Rician fading channels. Moreover, all simulation

results are obtained in an urban environment.

In Fig. 2, we plot the outage probability of the considered

system model with and without the RIS. It is observed that the

analytical results are in good agreement with the simulation re-

sults, which indicates that our proposed statistical distribution

is accurate. By increasing h, the outage probability decreases

ﬁrst and then increases. When his small, in fact, increasing

hincreases the possibility of LoS transmission and this, in

turn, improves the link reliability. By continuing to increase

h, a larger path loss is obtained and it dominates the system

performance.

In Fig. 3, we plot the outage probability curves versus ρfor

the considered dual-hop UAV system with and without RISs.

From Fig. 3, we observe that the system outage probability

decreases ﬁrst and then increases as ρincreases. As expected,

we see that there is an optimal ρfor a ﬁxed h. The main reason

5

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

ρ

10-4

10-3

10-2

10-1

100

Outage Probability

Analysis

Simulation

Without RIS

With RIS

N= 3

N= 6

Fig. 3. Outage probability versus ρfor a dual-hop UAV system with and

without RISs.

0 1 2 3 4 5 6 7 8 9 10

h[km]

10-4

10-3

10-2

10-1

100

Average BER

Simulation

Analysis

N= 2

N= 4

Without RIS

With RIS

Fig. 4. Average BER versus hfor a dual-hop UAV system with and without

RISs.

for this trend is that the outage probability of the ﬁrst hop is

very small for small values of ρ, which is due to the use of

the RIS. Thus, the outage performance is mainly determined

by the second hop. Therefore, increasing ρimplies that the

UAV becomes close to the destination and the path-loss of

the second-hop becomes small, which results in a smaller

outage probability. If ρis increased further, however, the ﬁrst

hop dominates the system performance. From Fig. 3, we can

observe that the optimal position of the UAV for the system

without the RIS is in the middle between S and D. For the

system with the RIS, on the other hand, the UAV needs to be

closer to D in order to obtain the best outage performance.

In Fig. 4, the average BER performance is illustrated when

p= 1 and q= 1. As expected, simulation and analytical

results match very well. Similar to the observations in Fig. 2,

the average BER ﬁrst decreases and then increases when h

increases, and an optimal height point can be found.

In Fig. 5 (a), we plot the outage probability versus the

SNR for ¯γ1= ¯γ2= ¯γ. As expected, the simulation results

are consistent with the analytical results. In addition, at large

SNRs, the asymptotic results are close to the exact ones.

Interestingly, we can observe that a system with large Nhas

5 10 15 20 25 30 35 40 45 50

¯γ2dB

10-4

10-3

10-2

10-1

100

Outage Probability

Simulation

Analysis

Asymptotic

5 10 15 20 25 30 35 40 45 50 55 60

¯γdB

10-3

10-2

10-1

100

Outage Probability

Analysis

Simulation

Asymptotic

Without RIS

N= 2, 3, 4

With RIS

(b)

(a)

N= 3, 5, 7

With RIS Without RIS

Fig. 5. Outage probability versus SNR for a dual-hop UAV system with and

without RISs.

5 10 15 20 25 30 35 40 45

¯γ2dB

10-4

10-3

10-2

10-1

100

Average BER

Simulation

Analysis

Asymptotic

10 15 20 25 30 35 40 45 50 55

¯γdB

10-4

10-3

10-2

10-1

100

Average BER

Simulation

Analysis

Asymptotic

With RIS

(a)

(b)

Without RIS

N= 2, 3, 4

N= 2, 4, 6

With RIS Without RIS

Fig. 6. Average BER versus SNR for a dual-hop UAV system with and

without RISs.

the same outage performance at high SNRs, like N=5=7.

The fundamental reason is that the system outage probability

at high SNR is mainly determined by the A2G link since the

outage probability of the G2A link is small due to the use of

the RIS. In Fig. 5 (b), we plot the outage probability versus

¯γ2for ¯γ1=30dB. For a ﬁxed ¯γ1, one can see that the outage

performance tends to a constant for large values of ¯γ2. The

reason is that the outage probability of the A2G link PA

out2in

Eq. (18) tends to zero when ¯γ2→ ∞, and the whole system

performance mainly depends on PA

out1. More speciﬁcally, for

a ﬁxed ¯γ1and a large ¯γ2, the system overall SNR γis

6

10 15 20 25 30 35 40 45 50 55 60

¯γdB

0

2

4

6

8

Average Capacity

Simulation

Bound

10 15 20 25 30 35 40 45

¯γ2dB

0

1

2

3

4

5

Average Capacity

Simulation

Bound

N= 2, 4, 6

With RIS

Without RIS

(a)

(b)

N= 2, 3, 4

With RIS

Without RIS

Fig. 7. Average capacity versus SNR for a dual-hop UAV system with and

without RISs.

determined by γ1due to the fact that γ= min {γ1, γ2}.

Similar to Fig. 5, we plot the BER performance in Fig. 6.

Similar observations as those obtained from Fig. 5 can be

obtained.

In Fig. 7 (a), we plot the upper bounds of the average capaci-

ty versus SNR for different values of Nwhen ¯γ1= ¯γ2= ¯γ. As

expected, the upper bounds provide accurate estimates of the

average capacity Moreover, one can see that the variation of

the capacity for large values of Nis small, since system SNR

depends on min {γ1, γ2}. Increasing Nresults in large γ1,

which in turn makes the system performance only dependent

on γ2. It is worth nothing that γ2is not dependent on Nand

an asymptotic value of the capacity if reached for large N. In

Fig. 7 (b), we plot the upper bound of the average capacity

versus ¯γ2when ¯γ1=20dB. Similar observations as for Fig.

5 and Fig. 6 can be made.

V. CONCLUSION

In this paper, a DF-based RIS-assisted UAV communication

system was proposed and analyzed in terms of outage prob-

ability, average BER, and average capacity. For a ﬁxed ¯γ1,

the results show that RISs can signiﬁcantly reduce the aver-

age BER, and improve the coverage probability, the average

capacity, and the reliability of the considered system model.

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