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On the BER Performance of RIS-enhanced NOMA-assisted Backscatter Communication under Nakagami-m Fading

Authors:

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.
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,
grCM×1
denotes the channel
response matrix between the BR and the RIS, and
fiCM×1
is the channel response matrix from the
i
-th BSN to the RIS.
Similarly,
Θ=diag β1e1, β2ej θ2. . . βMeMCM×M
is a diagonal matrix, where
βm[0,1]1
is the amplitude
reflection coefficient and
θmΦn
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,,...,(L1)∆}
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
emgr,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=MM1
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
mCi
emgr,mfi,m
| {z }
Coherently optimized
RIS elements for i-th BSN
+X
kCi
ekgr,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
mCi
emgr,mfi,m =X
mCi
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
˜x1S
ypΓ1PThb,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
˜x2S
(ypΓ1PThb,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
ϕΦ2|ϕθm|,(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.
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