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On the BER Performance of RIS-enhanced

NOMA-assisted Backscatter Communication under

Nakagami-mFading

Muhammad Usman∗, Sarah Basharat∗, Haris Pervaiz†, Syed Ali Hassan∗, and Haejoon Jung‡

∗School of Electrical Engineering and Computer Science (SEECS), NUST, Pakistan

†School of Computing and Communications, Lancaster University, U.K

‡Dept. of Electronic Engineering, Kyung Hee University, Seoul, South Korea

Email: {musman.bee18seecs, sarah.phdee21seecs, ali.hassan}@seecs.edu.pk,

h.b.pervaiz@lancaster.ac.uk, haejoonjung@khu.ac.kr

Abstract—Backscatter communication (BackCom) has been

envisioned as a prospective candidate for enabling the sustained

operation of battery-constrained Internet-of-Things (IoT) devices.

This approach involves the transmission of information by a

backscatter node (BSN) through passive reﬂection and modulation

of an impinging radio-frequency (RF) signal. However, the

short operational range and low data rates of contemporary

BackCom systems render them insufﬁcient on their own to

provide ubiquitous connectivity among the plethora of IoT devices.

Meanwhile, wireless networks are rapidly evolving towards

the smart radio paradigm. Thus, to enhance the coverage

range and capacity, reconﬁgurable intelligent surfaces (RISs)

can be incorporated into the existing BackCom systems. RISs

employ passive reﬂective elements to adaptively conﬁgure the

stochastic wireless environment in a cost-effective and energy-

efﬁcient manner. Furthermore, non-orthogonal multiple access

(NOMA) can be exploited to improve the spectral efﬁciency of

the BackCom systems. In this paper, we present the design and

bit error rate (BER) analysis of an RIS-enhanced NOMA-assisted

bistatic BackCom system under Nakagami-

m

fading channel.

Our extensive simulation results reveal the effectiveness of the

proposed system over the conventional NOMA-assisted BackCom

system without RIS, and demonstrate the impact of various factors,

including the power-reﬂection coefﬁcients, RIS phase-shift designs,

number of reﬂecting elements, RIS location, and split factor, on

the BER performance of the proposed RIS-assisted system.

Index Terms—Internet-of-Things (IoT), backscatter commu-

nication (BackCom), reconﬁgurable intelligent surfaces (RISs),

non-orthogonal multiple access (NOMA), phase-shift designs, bit

error rate (BER).

I. INTRODUCTION

The sixth-generation (6G) of wireless communication aims

to fulﬁll extremely high data rate and ultra-low latency require-

ments while supporting massive connectivity for empowering

the diverse paradigms of wireless communications. The Internet-

of-things (IoT) paradigm is envisioned as one of the potential

candidates for the provision of ubiquitous connectivity among

the massive number of heterogeneous devices [1]. Nevertheless,

despite the rapid evolution, the energy management of IoT

devices is one of the crucial challenges hindering their large-

scale deployment. Conventional battery-based solutions are

undesirable for the sustainable operation of IoT nodes in

case of deployment in an inaccessible environment and due

to the associated cost-prohibitive system maintenance [2].

Consequently, various energy harvesting techniques are under

investigation to overcome these challenges. Energy harvesting

is a practical and viable approach for IoT networks because

of the low energy requirements of IoT devices [2]. In this

regard, backscatter communication (BackCom) has emerged as

one of the promising energy harvesting solutions for powering

the massive number of IoT devices in an energy-efﬁcient and

cost-effective manner [3], [4].

BackCom allows the passive backscatter nodes (BSNs) to

deliver information by modulating and reﬂecting the incident

radio-frequency (RF) signal via the antenna impedance mis-

match. Despite the extensive research on BackCom systems,

the issue of limited coverage range and data rates persists

[5], [6]. These limitations, along with the stochastic nature of

wireless channels, are impeding their large-scale deployment.

Therefore, some mechanism is required to support the massive

connectivity and high data rate requirements while adapting

to the dynamic nature of the wireless channel. In this regard,

reconﬁgurable intelligent surfaces (RISs) have recently emerged

as a promising new paradigm for the adaptive conﬁguration of

the radio propagation environment [4], [7].

Speciﬁcally, an RIS is a planar surface which is composed

of a massive number of low-cost passive reﬂective elements,

such as PIN diodes and printed dipole arrays, each capable of

intelligently inducing a phase shift and/or amplitude change in

the incident signal independently. Thus, the wireless channel

between the communication terminals can be reconﬁgured

to achieve the desired channel response. Through intelligent

phase-shifts, RIS can either boost the signal to improve the

received signal-to-noise ratio (SNR), or attenuate the signal

to mitigate the interference. RISs have conformal geometry,

hence, they can be easily mounted on environmental objects,

such as building facades, walls, and ceilings [8].

The aforementioned features of RISs, and their compati-

bility with the existing wireless systems, make them an ideal

candidate to assist BackCom systems [9]. The RIS-assisted

bistatic BackCom system was ﬁrst studied in [10], where

the transmit beamforming vector at the CE, the RIS phase

shifts, and the reﬂection coefﬁcient at the BSNs, were jointly

optimized to minimize the transmit power. In [11], performance

analysis of RIS-aided BackCom system was studied, and

the symbol error probability (SEP) for coherent and random

phase-shift designs of RIS was analytically investigated. The

authors in [12] investigated the joint optimization of reﬂection

coefﬁcients and RIS phase shifts for an RIS-enhanced NOMA-

assisted BackCom system. Moreover, a deep reinforcement

learning (deepRL)-based approach for the joint optimization of

reﬂection coefﬁcients and RIS phase shifts for an RIS-assisted

ambient BackCom system was presented in [13].

Different from the aforementioned works, in this work, we

consider an RIS-enhanced NOMA-assisted bistatic BackCom

system. Power-domain NOMA is a promising multiple access

technique, which serves multiple users on the same orthogonal

resource block, resulting in improved spectral efﬁciency,

massive connectivity, and better user fairness over the OMA

techniques [4], [14]. We consider two users per NOMA cluster

to ensure low complexity during the successive interference

cancellation (SIC) process. In order to better exploit power-

domain NOMA (PD-NOMA), the reﬂection coefﬁcients of

the BSNs are assigned different values to establish a higher

channel gain difference. The proposed system results in an

improved bit error rate (BER) performance as compared to

the conventional NOMA-assisted BackCom system. The main

contributions of this paper are summarized as follows.

•

We propose an RIS-enhanced NOMA-assisted BackCom

system, where an RIS assists the backscattering commu-

nication between the BSNs and the BR.

•

The BER performance of the RIS-enhanced NOMA-

assisted bistatic BackCom system is studied under

Nakagami-

m

fading and RIS elements splitting approach.

•

Our extensive simulation results show the impact of

various factors, namely the transmit SNR, power-reﬂection

coefﬁcients, RIS phase-shift designs, number of reﬂecting

elements, RIS location, and split factor, on the BER

performance of RIS-enhanced NOMA-assisted BackCom

system.

The rest of the paper is organized as follows. In Sec. II, the

system model of the proposed RIS-enhanced NOMA-assisted

BackCom system is described. Sec. III provides the simulation

results to evaluate the performance of the proposed system.

Finally, conclusions are presented in Sec. IV.

II. SY ST EM MO DE L

As illustrated in Fig. 1, we consider an RIS-enhanced

NOMA-assisted bistatic BackCom system consisting of a

carrier emitter (CE),

I

backscatter nodes (BSNs), a backscatter

receiver (BR), and an RIS with

M

passive reﬂective elements.

Each passive reﬂective element has the capability to scatter

the impinging signal with an induced phase-shift. We assume

that each of the BSNs, BR, and CE is equipped with a single

BSN-2

BSN-1

Carrier emitter

(CE)

Backscatter receiver

(BR)

RIS

Continuous-wave signal Backscattered signal

h

f,1

h

f,2

f1

f2

gr

hd,1

hd,2

Fig. 1. An illustration of RIS-enhanced NOMA-assisted BackCom system

with I= 2.

antenna. Let

I

=

{1, . . . , I }

denotes the set of BSNs and

I

=

| I |

denotes the maximum number of BSNs, where

I

≥2

and

| I |

is the cardinality of set

I

. Since the hardware

complexity and processing delay due to SIC increases with an

increasing number of BSNs per cluster, therefore, in this work,

we assume I=2.

A. BackCom Model

The BSNs transmit their data by modulating the

continuous-wave (CW) signal generated by the CE. The pro-

posed system has two distinct transmission phases: (i) forward

or excitation phase, where the CE transmits the CW signal

to the BSNs; (ii) backscattering phase, where the BSNs

backscatter the modulated signal towards the BR through both

the direct and RIS reﬂected links.

1) Training Phase: In a single cluster BackCom system

with two BSNs, a training phase is employed to distinguish

between the strong and weak BSN based on the channel

state information (CSI). For this, each BSN backscatters the

CW signal towards the BR in a separate time slot with the

same value of power reﬂection coefﬁcient. By comparing the

instantaneous CSI obtained from the received backscattered

signals, the BR recognizes the strong/weak BSN. The BSNs

then adjust their reﬂection coefﬁcients, based on their CSI at

the BR, to employ PD-NOMA.

2) Operating States: The BSNs in the proposed system

operate in one of the two states, either the active state or the

energy harvesting state. We assume that the CE continuously

transmits the CW signal. In the active state, the BSNs

backscatter the modulated signal to the BR, whereas in the

energy harvesting state, the BSNs harvest the energy from

the CW signal. The harvested energy is stored in a battery to

power the internal circuitry and perform sensory functions.

3) BPSK Modulation: The modulation of the incident

CW signal is done by varying the antenna impedance of

the backscatter device, which in turn changes the value

of the reﬂection coefﬁcient. The BPSK modulation scheme

is considered as it aligns with the complexity and energy

constraints of the low-power BSNs. For BPSK, the antenna

impedance is switched between the two impedance states,

which generates two reﬂection coefﬁcients with the same

magnitude but a phase-shift of 180°.

B. Signal Model

The backscattered signal by the i-th BSN is given by

si=pΓiPThf,i xi,(1)

where

PT

is the CE’s transmit power,

si

denotes the backscat-

tered signal of the

i

-th BSN,

Γi

and

xi

denote the reﬂection

coefﬁcient and modulated information symbol of the

i

-th BSN,

respectively. Similarly, in (1),

hf,i

is the fading-free forward

channel coefﬁcient, modeled by the propagation path loss

qdn

f,i

,

where

df,i

is the distance between the CE and the

i

-th BSN

and

n

is the path loss exponent. Here, the assumption of a

fading-free channel model is reasonable since the BSNs are

in close proximity of the CE and have a strong line-of-sight

(LoS) link with the CE. In addition, since the RIS is deployed

close to the BR, therefore, the RIS reﬂected signal from the

CE to the BSNs will be substantially attenuated due to the

multiplicative path loss of the reﬂected link, and hence can be

considered negligible. The received signal at the BR is given

by

y=

I

X

i=1 pΓiPThf,i

direct link

z }| {

hd,i

qdn

d,i

+

RIS reﬂected link

z }| {

gH

rΘfi

qdn

b,idn

I

xi+w, (2)

where

hd,i

and

dd,i

denote the channel coefﬁcient and the

distance from the

i

-th BSN to the BR, respectively. Similarly,

in (2),

db,i

and

dI

are the distances from the the IRS to the

i

-th

BSN and CE, respectively,

gr∈CM×1

denotes the channel

response matrix between the BR and the RIS, and

fi∈CM×1

is the channel response matrix from the

i

-th BSN to the RIS.

Similarly,

Θ=diag β1ejθ1, β2ej θ2. . . βMejθM∈CM×M

is a diagonal matrix, where

βm∈[0,1]1

is the amplitude

reﬂection coefﬁcient and

θm∈Φn

is the phase-shift induced

by the

m

-th RIS element, where

Φn

denotes the feasible set

of phase-shifts. We consider two feasible sets of phase-shifts

Φ1

and

Φ2

, where

Φ1= [0,2π)

is the set of continuous phase-

shifts, and

Φ2={0,∆,...,(L−1)∆}

is the set of discrete

phase-shifts, obtained by uniformly quantizing the interval

[0,2π)

, where

∆=2π/L

, and

L= 2b

denotes the discrete

phase-shift levels which have a

b

-bit resolution.

w∼ CN (0, σ2)

is the additive white Gaussian noise (AWGN) with zero mean

and variance σ2.

It is assumed that all RIS elements reﬂect the impinging

signals independently; consequently, there is no signal coupling

in the reﬂection by the adjacent RIS elements. As such, the

RIS reﬂected link can equivalently be represented as

gH

rΘfi=

M

X

m=1

ejθmgr,m fi,m,(3)

1

We assume that the RIS does not attenuate the amplitude of the incident

carrier wave. Hence, in the sequel of this paper, we set

β1=β2=··· =

βM= 1.

where

gr,m

and

fi,m

denote the

m

-th element of

gH

r

and

fi

,

respectively, and

θm

is the phase of the

m

-th diagonal element

of

Θ

. In this work, we consider the surface partitioning concept

[14], where the total number of RIS elements are split between

the IBSNs. The distribution of elements is speciﬁed by the

split factor α∈[0,1], which is deﬁned as

α=M1

M,(4)

where

M1

denotes the number of elements, rounded up to

the nearest integer, conﬁgured for enhancing the performance

of BSN-

1

by a coherent combination of the direct and RIS

reﬂected signals at the BR. On the other hand,

M2=M−M1

are the elements conﬁgured for BSN-

2

signal. Therefore, for

the case of partitioned RIS, the RIS reﬂected link of the

i

-BSN

can be written as

gH

rΘfi=X

m∈Ci

ejθmgr,mfi,m

| {z }

Coherently optimized

RIS elements for i-th BSN

+X

k∈Ci

ejθkgr,k fi,k,

| {z }

Randomly conﬁgured

RIS elements for i-th BSN

(5)

where

Ci

denotes the set of elements conﬁgured for the

i

-th

BSN, while

Ci

denotes the set of elements which are not

conﬁgured for the

i

-th BSN, and thus randomly combine the

signal of

i

-th BSN. For coherently optimized RIS elements,

θmis selected as

θm= arg[hd,i]−arg[gr,m fi,m].(6)

The coherently optimized part of the RIS reﬂected link

can be written equivalently as

X

m∈Ci

ejθmgr,mfi,m =X

m∈Ci

ejarg[hd,i]

gr,m

fi,m

.(7)

C. Successive Interference Cancellation for Detection

In order to decode the signals transmitted by the BSNs,

SIC is carried out at the receiver. The effects of the forward

channels

hf,i

’s are compensated in the transmit SNR of both

BSNs and are thus omitted in further analysis. Without the loss

of generality, for the case I= 2, it is assumed that the BSN-

1

has a higher channel gain than the BSN-

2

i.e.,

hb,1

>

hb,2

,

where

hb,i =hd,i

√dn

d,i

+gH

rΘfi

√dn

b,idn

I

is the backward channel of the

i

-th BSN. Following the SIC scheme, the BR ﬁrst decodes the

data symbol of the strong BSN, i.e., BSN-

1

, and then subtracts

it from the composite received signal to detect the data symbol

of the weak BSN, i.e., BSN-

2

. Hence, the optimal decoding

order is in the order of decreasing channel gains.

The receiver decodes the BSN-

1

’s signal using the

maximum likelihood detector (MLD), while treating the BSN-

2

’s signal as inter-user interference (IUI). By assuming the

availability of perfect CSI at the receiver, the MLD for BSN-

1

’s

symbol can be described as

ˆx1= arg min

˜x1∈S

y−pΓ1PThb,1˜x1

2,(8)

TABLE I

SIMULATION PARAMETERS

Parameters Values

CE’s transmit power PT= 30 dBm

Nakagami parameters

mhd1= 5, mf1= 5

mhd2= 2, mf2= 2

mgr= 3

Number of RIS elements M= 32

Reﬂection coefﬁcients Γ1= 0.8

Γ2= 0.3

Split factor α= 0.6

Path loss exponent n= 2

where

ˆx1

and

˜x1

denote the estimated data symbol and the

possible trial values of

x1

, respectively, and

S={+1,−1}

is

the set of all possible constellation points for BSN-

1

. If the

BSN-

1

’s symbol is detected correctly, there is no IUI while

decoding BSN-

2

’s symbol. However, in the case of incorrect

detection of BSN-

1

’s symbol, a bit error occurs that results in

an error propagation from BSN-

1

, in the form of IUI, during the

decoding of BSN-

2

’s symbol. The MLD for BSN-

2

’s symbol

can then be described as

ˆx2= arg min

˜x2∈S

(y−pΓ1PThb,1ˆx1)−pΓ2PThb,2˜x2

2,(9)

where

ˆx2

and

˜x2

denote the estimated data symbol and the

possible trial value of

x2

, respectively. A bit error occurs when

ˆx2=x2.

III. PERFORMANCE ANALYSIS

In this section, we evaluate the performance of the

proposed RIS-NOMA-BackCom system through numerical

simulations and demonstrate the performance improvement

over the conventional NOMA-BackCom system. All wireless

channels are assumed to be mutually independent and follow

Nakagami-m fading distribution, as it provides ﬂexibility in

the description of LoS and NLoS links. As in [12], we assume

that the CE, RIS, and the BR are located at coordinates

(0,10)

m,

(50,10)

m, and

(70,10)

m, respectively. BSN-

1

and BSN-

2

are assumed to be located at

(20,25)

m and

(20,−5)

m,

respectively. Moreover, in order to ensure successful decoding

at the receiver, appropriate values for the reﬂection coefﬁcients,

i.e.,

Γ1

and

Γ2

, and the split factor, i.e.,

α

, are assumed. Unless

mentioned otherwise, the simulation parameters are enlisted in

Table I.

A. Performance Enhancement via RIS Integration

In Fig. 2, we evaluate the BER performance against the

transmit SNR of each BSN to demonstrate the performance

gain achieved by incorporating an RIS into the NOMA-assisted

BackCom system. As illustrated in Fig. 2, the RIS-enhanced

system outperforms the conventional NOMA-BackCom system

without an RIS. This is due to the fact that the direct and

0 5 10 15 20 25 30

Transmit SNR (dB)

10-4

10-3

10-2

10-1

100

BER

NOMA-BackCom BSN-1 [2]

NOMA-BackCom BSN-2 [2]

RIS-NOMA-BackCom BSN-1

RIS-NOMA-BackCom BSN-2

Fig. 2. BER plot of conventional NOMA-BackCom system and RIS-NOMA-

BackCom system with M=24.

0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85

Split Factor,

10-8

10-6

10-4

10-2

100

BER

RIS-NOMA-BackCom BSN-1

RIS-NOMA-BackCom BSN-2

1 = 0.8, 2 = 0.3

Fig. 3. BER plot for varying split factor,

α

, with

M

=

48

and SNR =

5

dB.

RIS reﬂected signals are added coherently at the BR, which

results in improved SNR of the RIS-enhanced system. However,

the performance improvement in the low SNR regime is not

that eminent due to the severe product-distance path loss

experienced by the RIS reﬂected link.

B. Impact of Elements Splitting

The performance of RIS-enhanced system highly depends

on the number of conﬁgured RIS elements for each BSN. In

this regard, the BER of BSN-1 and BSN-2 is plotted against

the split factor, i.e.,

α

, in Fig. 3. It can be observed that the

performance of BSN-

1

improves with the increase in

α

since the

allocation of more RIS elements to BSN-

1

and fewer elements

to BSN-

2

increases the difference between the received signal

strength of both BSNs. This leads to a lower IUI during the

decoding of BSN-

1

’s signal, which increases the probability of

detecting the BSN-

1

’s data symbol correctly. Consequently, the

0 5 10 15 20

SNR (dB)

0

0.2

0.4

0.6

0.8

1

1.2

Normalized total effective bits of both BSNs

Continuous phase-shift design

Discrete phase-shift design

Random discrete phase-shift design

Fig. 4. Effectively decoded bits for BSN-1 and BSN-2 against the transmit

SNR for different phase-shift designs and M= 24.

performance of BSN-

2

also improves as there is less probability

of error propagation from BSN-1during BSN-2’s decoding.

However, despite the low probability of error propagation

from BSN-

1

, the performance of BSN-

2

deteriorates when

α

is higher than

0.60

. This is because the increase in

α

above the optimal value reduces the strength of the BSN-

2

’s

signal signiﬁcantly, which increases the probability of incorrect

decoding. Furthermore, it can be observed that for transmit

SNR of

5

dB, the optimal value of a split factor is

0.60

since

it provides the lowest BER for both BSNs.

C. Impact of Phase-shift Designs

Fig. 4 compares the performance of the proposed RIS-

NOMA-BackCom system for continuous, discrete, and random

discrete phase-shift designs. In the random discrete phase-shift

design, the phase-shift for each element is randomly chosen

from the set of discrete phase-shifts, i.e.,

Φ2

, while in discrete

phase-shift design, the optimal phase-shifts are given by

ψm= arg min

ϕ∈Φ2|ϕ−θm|,(10)

where

ψm

is the optimal value of the discrete phase-shift for

the m-th element of RIS.

As illustrated in Fig. 4, the continuous phase-shift ap-

proach outperforms the discrete phase-shift designs for a given

number of RIS elements and transmit SNR. However, it is

infeasible to realize the continuous phase-shifts due to the high

hardware cost and complexity [7]. Moreover, the discrete phase-

shift design with

1

-bit resolution has a better performance than

the random discrete phase-shift design with the same resolution.

In addition, the performance of discrete phase-shift design can

be enhanced by increasing the number of quantization levels.

D. Effect of the RIS Elements and Location

The system performance highly depends on the number

of reﬂective elements and the RIS location. In this regard,

0 5 10 15 20 25 30

Transmit SNR (dB)

10-4

10-3

10-2

10-1

100

BER

RIS-NOMA-BackCom BSN-1

RIS-NOMA-BackCom BSN-2

(40, 10) m

(50, 10) m

Fig. 5. BER plot for varying location of RIS with M=24 and α=0.6.

0 5 10 15 20 25 30

Transmit SNR (dB)

10-4

10-3

10-2

10-1

100

BER

RIS-NOMA-BackCom BSN-1

RIS-NOMA-BackCom BSN-2

M = 24

M = 32

Fig. 6. BER plot for varying number of RIS elements with RIS coordinates

(50,10) m and α=0.6.

Fig. 5 compares the BER performance for

M= 24

and RIS

located at

(40,10)

and

(50,10)

m. It can be observed that

the BER performance improves by deploying RIS closer to

the BR, owing to the reduced path loss of the RIS reﬂected

backward link. The effect of total number of RIS elements

on the BER performance for the RIS located at

(50,10)

m is

illustrated in Fig. 6. As expected, the performance improves

when the number of RIS elements is increased. For instance,

for

20

dB transmit SNR, the BER performance improves by

approximately

80%

when RIS elements increase from

M= 24

to M= 32.

E. Effects of the Reﬂection Coefﬁcients

Fig. 7 illustrates the effect of reﬂection coefﬁcients on

the normalized average of total effectively decoded bits, which

corresponds to the sum of non-erroneous transmission of BSNs’

bits averaged over the total number of transmitted bits for both

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Reflection coefficient, 2

0.7

0.75

0.8

0.85

0.9

0.95

1

Normalized total effective bits of both BSNs

SNR = 0 dB

SNR = 5 dB

SNR = 10 dB

Fig. 7. The normalized average of effectively decoded bits for BSN-

1

and

BSN-2against Γ2with Γ1= 0.8.

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Reflection coefficient, 2

0.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

Split factor,

BER of BSN-1

BER of BSN-2

0.05

0.1

0.15

0.2

0.25

Fig. 8. BER contour plot of BSN-1 and BSN-2 for varying values of

Γ2

and

αwith Γ1= 1.

BSNs. It can be observed that the optimal

Γ2

for transmit

SNRs of 0, 5, and 10 dB is 0.38, 0.30, and 0.30, respectively.

Moreover, it can be seen that a greater value of

Γ2

is required

to successfully decode the BSNs’ signal at low transmit SNRs.

In Fig. 8, a contour plot of BER is plotted by varying the

reﬂection coefﬁcient,

Γ2

, and split factor,

α

, while keeping

Γ1

as

1

. It can be observed that for a ﬁxed value of

Γ2

, there exists

a range of

α

values which can provide an acceptable BER

performance. Moreover, as mentioned before, an increase in

Γ2

deteriorates the system performance due to IUI in the decoding

of BSN-

1

’s signal. Moreover, the best BER performance is

achieved when Γ2is approximately 0.35 and αis 0.6.

IV. CONCLUSION

In this work, we presented an RIS-enhanced NOMA-

assisted BackCom system for realizing the high data rate

and massive connectivity requirements of 6G IoT networks.

Speciﬁcally, we studied the BER performance of the RIS-

enhanced NOMA-assisted bistatic BackCom system under

Nakagami-mfading and RIS elements splitting approach. Our

extensive simulation results clearly illustrated the performance

gain achieved by integrating an RIS and demonstrated that

the system performance can be signiﬁcantly enhanced by

increasing the number of reﬂecting elements, or by selecting the

optimal values for split factor and power reﬂection coefﬁcients

without increasing the CE transmit power. Hence, this entails

an optimization study as a future work where the split factor

and the reﬂection coefﬁcients can be jointly optimized for the

provision of maximum system performance in terms of BER.

REFERENCES

[1]

D. C. Nguyen, M. Ding, P. N. Pathirana, A. Seneviratne, J. Li, D. Niyato,

O. Dobre, and H. V. Poor, “6G Internet of Things: A comprehensive

survey,” IEEE Internet of Things Journal, vol. 9, no. 1, pp. 359–383,

2022.

[2]

A. W. Nazar, S. A. Hassan, H. Jung, A. Mahmood, and M. Gidlund, “BER

analysis of a backscatter communication system with non-orthogonal

multiple access,” IEEE Transactions on Green Communications and

Networking, vol. 5, no. 2, pp. 574–586, 2021.

[3]

J. Guo, X. Zhou, S. Durrani, and H. Yanikomeroglu, “Design of non-

orthogonal multiple access enhanced backscatter communication,” IEEE

Transactions on Wireless Communications, vol. 17, no. 10, pp. 6837–

6852, 2018.

[4]

S. Basharat, S. Ali Hassan, H. Pervaiz, A. Mahmood, Z. Ding, and

M. Gidlund, “Reconﬁgurable intelligent surfaces: Potentials, applications,

and challenges for 6G wireless networks,” IEEE Wireless Communica-

tions, vol. 28, no. 6, pp. 184–191, 2021.

[5]

A. W. Nazar, S. A. Hassan, and H. Jung, “BER analysis of a NOMA

enhanced backscatter communication system,” in GLOBECOM 2020 -

2020 IEEE Global Communications Conference, 2020, pp. 1–6.

[6]

J.-P. Niu and G. Y. Li, “An overview on backscatter communications,”

Journal of Communications and Information Networks, vol. 4, no. 2, pp.

1–14, 2019.

[7]

Q. Wu and R. Zhang, “Towards smart and reconﬁgurable environment:

Intelligent reﬂecting surface aided wireless network,” IEEE Communica-

tions Magazine, vol. 58, no. 1, pp. 106–112, 2020.

[8]

Q. Wu, S. Zhang, B. Zheng, C. You, and R. Zhang, “Intelligent reﬂecting

surface-aided wireless communications: A tutorial,” IEEE Transactions

on Communications, vol. 69, no. 5, pp. 3313–3351, 2021.

[9]

S. Basharat, S. A. Hassan, A. Mahmood, Z. Ding, and M. Gidlund,

“Reconﬁgurable intelligent surface-assisted backscatter communication:

A new frontier for enabling 6G IoT networks,” IEEE Wireless Commu-

nications, pp. 1–8, 2022.

[10]

X. Jia, J. Zhao, X. Zhou, and D. Niyato, “Intelligent reﬂecting surface-

aided backscatter communications,” in GLOBECOM 2020 - 2020 IEEE

Global Communications Conference, 2020, pp. 1–6.

[11]

W. Zhao, G. Wang, S. Atapattu, T. A. Tsiftsis, and X. Ma, “Performance

analysis of large intelligent surface aided backscatter communication

systems,” IEEE Wireless Communications Letters, vol. 9, no. 7, pp.

962–966, 2020.

[12]

J. Zuo, Y. Liu, L. Yang, L. Song, and Y.-C. Liang, “Reconﬁgurable

intelligent surface enhanced NOMA assisted backscatter communication

system,” IEEE Transactions on Vehicular Technology, vol. 70, no. 7, pp.

7261–7266, 2021.

[13]

X. Jia and X. Zhou, “IRS-assisted ambient backscatter communications

utilizing deep reinforcement learning,” IEEE Wireless Communications

Letters, vol. 10, no. 11, pp. 2374–2378, 2021.

[14]

B. Tahir, S. Schwarz, and M. Rupp, “Outage analysis of uplink IRS-

assisted NOMA under elements splitting,” in 2021 IEEE 93rd Vehicular

Technology Conference (VTC2021-Spring), 2021, pp. 1–5.