Performance enhancement of wireless sensor network by using non-orthogonal multiple access and sensor node selection schemes

Indonesian Journal of Electrical Engineering and Computer Science
Vol. 21, No. 2, February 2021, pp. 886~894
ISSN: 2502-4752, DOI: 10.11591/ijeecs.v21.i2.pp886-894  886
Journal homepage: http://ijeecs.iaescore.com
Performance enhancement of wireless sensor network by using
non-orthogonal multiple access and sensor node selection
schemes
Duy Hung Ha1
, Dac-Binh Ha2
, Van-Truong Truong3
, Van-Duc Phan4
, Q. S. Vu5
1
Wireless Communications Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang
University, Ho Chi Minh City, Vietnam
2,3
Faculty of Electrical-Electronic Engineering, Duy Tan University, Da Nang, Vietnam
4
Faculty of Automobile Technology, Van Lang University, Ho Chi Minh City, Vietnam
5
School of Engineering-Technology & Arts, Hong Bang International University, Ho Chi Minh City, Vietnam
Article Info ABSTRACT
Article history:
Received Jun 4, 2020
Revised Aug 3, 2020
Accepted Aug 11, 2020
In this paper, we investigate a relaying wireless sensor network (WSN) with
the non-orthogonal multiple access (NOMA) and sensor node selection
schemes over rayleigh fading. Precisely, the system consists of two sensor
clusters, a sink node, and an amplify-and-forward (AF) relay. These sensors
applying the NOMA and sensor node selection schemes transmit the sensing
data from the sensor clusters via the relay to the sink. We derived the
expressions of outage probability and throughput for two sensor nodes. We
also provide numerical results to examine the behavior of the system. Finally,
we verify the validity of our analysis by using the monte-carlo simulation.
Keywords:
Amplify-and-forward
Best sensor node selection
NOMA
Outage probability
Throughput
WSN
This is an open access article under the CC BY-SA license.
Corresponding Author:
Dac-Binh Ha
Faculty of Electrical-Electronic Engineering
Duy Tan University, Da Nang, Vietnam
Email: hadacbinh@duytan.edu.vn
1. INTRODUCTION
In the last decades, wireless sensor networks (WSN) have been widely applied in many fields, such
as monitoring environmental parameters in industry and agriculture, smart transport, smart grids, wearable
medical care devices [1-3]. The main advantage of wireless sensor networks is that the use of existing
infrastructure may not incur additional costs of wiring and equipment, and further, the cloud availability and
IoT protocol for a fast connection. However, WSN also contains many problems that need to be addressed,
typically data transmission between devices in the network when the scale of the network becomes very large
with a massive amount of data [4]. The non-orthogonal multiple access (NOMA) technique was proposed as
the best solution to solve the above problem when satisfying the very high data rate and massive connectivity
demand both in uplink and downlink transmissions [5-7]. NOMA can support multiple users at the same time
and the same frequency resource. In WSN, the uplink channel plays a critical role, as this is the path that the
sensors used to perform the task of transmitting the data they collect to the sink node. However, studies on
WSN using uplink NOMA are still relatively small [8-12]. In [8], the authors proposed WSN to use the
uplink NOMA application to measure parameters in agriculture. The sensors are divided into clusters and use
short-range multi-hop communication technology to transfer data to the sink node. The authors used the sum

Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752 
Performance enhancement of wireless sensor network by using non-orthogonal multiple… (Duy Hung Ha)
887
data rate and outage probability to examine the performance of the system. In [9], the uplink NOMA
multiuser model was proposed.
The base station is equipped with N antennas, and each user equipment unit has a single antenna, the
users are divided into two groups: robust set and weak set depending on the channel status. The paper
proposes that the power control scheme can maximize the sum capacity with a minimum target rate. Another
common problem with WSN is the transmission distance in the network. The sensor nodes located at the
network edge can only communicate with neighboring nodes and need support from the relay to
communicate with the sink node. Transition techniques combined with NOMA helps solve this problem in
WSN [13-25]. In [13], the authors proposed the uplink NOMA model for two users with the support of the
decode-and-forward relay to communicate with the base station. The paper goes into system analysis by
providing formulas for system probability and throughput based on critical parameters such as signal to noise
ratio, transmission power. In [16], two users communicated with the base station simultaneously with the
existence of a half-duplex relay employing the decode-and-forward scheme to assist the far user. With the
given target data rates, the authors proposed the method to determine the most optimal power allocation
factors.
Different from previous studies, in this study, we propose a NOMA scenario for the uplink of two
sensor node clusters, in which the two sensors apply the uplink NOMA scheme to transmit their information
to the sink via the AF relay. The proposed model uses the best combination of direct link and forward-link
selection mechanism based on the maximum signal to end-to-end noise ratio. To analyze the performance of
this system, we derive expressions of the outage probability and throughput by using the Gaussian-
Chebyshev quadratic. The numerical results will be calculated according to the main parameters:
transmission power, the number of sensor nodes to find ways to improve the performance of this system.
The remainder of this research can be formulated as follows. Section 2 presents the system model.
Section 3 analyzes the system performance. Section 4 shows the numerical results with some discussions.
Finally, Section 5 concludes the study.
2. SYSTEM MODEL
We consider an uplink NOMA relaying system for WSN as Figure 1. This system consists of two
sensor clusters P with N sensor nodes and Q with M sensor nodes, a relay node, and a sink. Two sensor nodes
selected respectively from P and Q transmit their messages to the sink node (S) with the help of a single AF
R. Assuming that the sensing data, i.e., confidential data, video data, from P is more important than the data,
i.e., humidity, temperature, from Q. Therefore, the best sensor node is selected from P (SNP*), meantime, the
sensor node of Q (SNQ) is randomly chosen to transmit their data to S via R. All sensor nodes, relay, and
sink have single-antenna and work in half-duplex mode. Suppose R has sufficient channel information from
the sensor cluster, thus according to the maximum channel power gain, R assigns a sensor node as the best
node SNP* from P to send sensing data to it.
Figure 1. System model
The proposed operating protocol for this system is given as follows:
Phase 1: SNP* and SNQ simultaneously transmit their signals (s1, s2) to R during the period of T/2, where T
denotes as transmission block time.

 ISSN: 2502-4752
Indonesian J Elec Eng & Comp Sci, Vol. 21, No. 2, February 2021 : 886 - 894
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Phase 2: During the remaining T/2 period, R amplifies and forwards the signal received from SNP* and
SNQ to S. Finally, S uses the successive interference cancellation (SIC) to detect the signal of SNP* and
SNQ to obtain the information of each node.
We describe in detail each phase mathematically as follows.
Phase 1: Apply NOMA scheme, SNP*, and SNQ simultaneously to transmit their signals (s1, s2) to R by using
their transmit power on the same frequency in the same period T/2.
The signal received at R has the following form:
1 2
1 1 1 2 2 1
1 2
,
P P
y h s h s n
d d
 
  
(1)
where hk (k = {1, 2}) are Rayleigh fading channel coefficients of links from SNP* and SNQ to R, respectively,
n1 is AWGN with zero mean and the variance of
2
 ,
2
2 ~ (0, )
n CN 
, d1 and d2 are the Euclidean distances of
SNP* and SNQ to R, respectively, and  represents the path-loss exponent.
Phase 2: Applying the AF scheme, the transmission signal at R has the transmission power PR can be
formulated by,
1.
R
y Gy

(2)
In particular, G is the relaying gain of the AF relay R, which is defined by the fact that the P3
bounds the transfer power of the relay. Therefore, G is given by,
3
2 2 2
1 1 1 2 2 2
.
| | / | | /
P
G
P h d P h d
 


 
(3)
Therefore, the signal received at S is written as,
3 1 2
2 1 1 2 2 1 2
2 2
3
,
Gh P P
y h s h s n n
d d
d
 

 
   
 
 
  (4)
where h3 and d3 are the Rayleigh fading channel coefficient and Euclidean distance of R and S, respectively
2
2 ~ (0, )
n CN 
.
Finally, S applies SIC to detect the signals of SNP* and SNQ. The process is as follows: s1 will be
detected first due to better channel condition, then separation of signal s2 by subtracting s1 from y2. The
instantaneous SINR for detecting s1 at S is given by,
 
1
2 2
2
1 1 3 1 3
2 2
2 2 2
2 2 2 3 3
2 2
1 3 1 3
2 2 2 2 2
2 3 2 3 1 1 2 2 3 3
/
/ /
,
1
s
PG h h d d
G P h d h d
h h
h h h h h
 
 

 
 
    

 

   
(5)
where
1 2 3
1 2 3
2 2 2
1 2 3
, , .
P P P
d d d
  
  
  
  
After subtracting s1 from y2, we obtain instantaneous SINR to detect s2 at S as follows,
2
2 2
2 3 2 3
2 2 2
1 1 2 2 3 3
.
1
s
h h
h h h
 

  

  
(6)
The Probability Density Function (PDF) and Cumulative Distribution Function (CDF) functions of
the random variable Xi (i = {1, 2}) are calculated as, respectively,

889
 
1
.
i
i
x
X
i
f x e 



(7)
  1 .
i
i
x
X
F x e 

 
(8)
Meanwhile, due to selecting the best channel of links P-R, the CDF and PDF of channel power gain
 
2
1 1
1
max i
i N
Y h
 

is given as follows,
   
1 1
1
0
1 1 ,
N
x ix
N
i
Y
i
N
F x e e
i
 
 

   
   
   
   
 

(9)
   
1 1 1
1
1 ( 1)
1
0
1 1
1
1 1 .
N i x
x x N
i
Y
i
N
N N
f x e e e
i
  
 
 

  

  
 
   
   
   
 

(10)
For the later calculation, we obtain the expression of CDF for
1 1
1
2 2 1
Y
U
X




as follows:
  1 1
1
1 1 1 1
1
2 2 1 1 2 2
Pr 1 ( 1) .
1
ix
N
i
U
i
N
Y
F x x e
i
X i x
 
 
   


   
    
   
   
 

(11)
 
 
1 1
1
1 1 1 2 2
2
1 1 1 2 2
1 1 2 2
1
( 1) .
ix
N
i
U
i
N
f x ie
i i x
i x
 
  
  
  



 
 
 
  
   


   

(12)
3. PERFORMANCE ANALYSIS
The outage probability Pout for detection of s1 and s2 are expressed as follows, respectively:
1
1
2
Pr 2 1
s
out s t
P  

 
   
 
  (13)
 
2
2
Pr
s
out s t
P  
 
(14)
Theorem 1.
Under quasi-static Rayleigh fading, the closed-form expression of Pout to detect s1 for this considered system
is given by,
 
1 1 3 3 1 1
1
1
2
1 1 2 2 1 1 2 2 2 2
2
1 1 1 1 2 2 2 2
1 ( 1) 1 ,
2
t
l
i i
N L
s i l l t l
out l
i l l t l l
N i i
P ie e a
i
L i i


      
             
         
 
 
 
 
 
 
 
    
   
 
 
 
 
   
 
(15)
where
1
ln ,
2
l
l
a



 
3 3
1
t t
 

 


,
2 1
cos
2
l
l
a
L


 
  
  .
Proof: See Appendix A.
Theorem 2.
Using quasi-static Rayleigh fading, the closed-form expression of the outage probability to detect s2 for this
considered system is given by

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890
 
2 2
2 2 3 3
2
1 1 2
2 1
3 3
2
0 1
2 2 1 1 3 3
1 1
1 ( 1) .
2 ( 1)
l
t N L
l
s i
out
i l l t l
N e a
N
P e
i
L i

  

   
  
        
 

 
 
 
 
 
 
 
  
 
 
 
 
(16)
Proof: See Appendix B.
The expressions of throughput for SNP* and SNQ are formulated as,
 
1
1
1
.
2
s
out
P

 

(17)
 
2
2
1
.
2
s
out
P

 

(18)
From (15)-(18), the closed-form expressions for SNP* and SNQ can be calculated, respectively.
4. NUMERICAL RESULTS AND DISCUSSION
We use the Monte Carlo simulation to verify the numerical results on the impact of key system
parameters, i.e., transmit power, number of sensor nodes, on the system performance [21-25]. The primary
simulation parameters are presented in Table 1. From Table 1, the distances of SNP* and SNQ to R and from
R to S are calculated as follows:
Table 1. Simulation parameters
Parameters Notation Typical Values
Environment Rayleigh
Transmit power of sensor nodes P1, P2 5, 10 dBm
Transmit power of the relay P3 0-20 dBm
The minimum required data rate  1 bps/Hz
The coordinate of S {x0, y0} {0,0}
The coordinate of SNP* {x1, y1} {1,0}
The coordinate of SNQ {x2, y2} {0,1}
The coordinate of R {x3, y3} {0.5,0.5}
The path-loss exponent  2
The parameters of the exponential distribution  1
Number of sensor nodes of P N 2, 4, 6
Number of sensor nodes of Q M 1
The complexity-vs-accuracy trade-off coefficient L 100
2 2
1 1 3 1 3
( ) ( )
d x x y y
   
(19)
2 2
2 2 3 2 3
( ) ( )
d x x y y
   
(20)
2 2
3 0 3 0 3
( ) ( )
d x x y y
   
(21)
Figure 2 depicts the outage probability Pout versus the transmit power of relay by varying the
transmit power of sensor nodes. As we observe from this Figure 2 that Pout to detect s1 and s2 decreases when
the transmit power increases. It means that the system performance can be improved by increasing the power
at the relay. However, the impact of transmit power of sensor nodes on the system performance is quite
different. Correctly, when increasing P1 we can see that
1
s
out
P
decreases and
2
s
out
P
increases, on the contrary,
1
s
out
P
increases and
2
s
out
P
decreases when P2 increases. It is explained that when increasing P1 the interference
affected the detection of s2 at S is much more serious; thus, it makes
2
s
out
P
increases. It is similar to the case of
increasing P2.

891
Figure 3 depicts the flux of SNP* and SNQ (τ1 and τ2) versus P3 with different transmit power of
sensor clusters. We see that τ1 and τ2 increase with increasing P3. It means that the performance of the system
improves as the transmit power at the relay node R increases. Figure 3 also shows that when the transmit
power of sensor cluster P decreases, the throughput τ1 decreases while the throughput τ2 increases.
Furthermore, vice versa, when reducing the transmit power of sensor cluster Q, the throughput τ2 decreases,
while throughput τ1 increases.
Figure 2. Pout versus the transmit power P3 with
different P1 and P2 (P1, P2)
Figure 3. Throughput  versus the transmit power P3
with different P1 and P2 (P1, P2)
Figure 4 and Figure 5 depict the effect of the number of sensor nodes at the sensor cluster P on the
outage probability and throughput, respectively. The results once again confirm that when increasing the
transmit power at R, the system performance improves, as the Pout decreases and τ increases.
Figure 4. Pout versus the transmit power P3 with different N
Figure 4 draws the increase in the number of sensor nodes N,
1
s
out
P
decreases, and
2
s
out
P
increases. This
can be explained as the more sensor nodes at P, and the more likely the system is to find the best sensor node
to carry out the data transmission of the sensor cluster P. The better the signal from the sensor cluster P, the
more interference it will generate for the sensor cluster Q, resulting in
2
s
out
P
increases.

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As shown in Figure 5, the number of sensor nodes N increases, τ1 increases, and τ2 decreases,
meaning that the performance of the sensor cluster P is improved while the performance of the sensor cluster
Q is worse. This conclusion is also consistent with the results obtained from Figure 5.
Figure 5. Throughput  versus the transmit power P3 with different N
5. CONCLUSION
In this work, we have investigated the multiuser uplink NOMA wireless sensor network with the
best user selection scheme. The closed-form expressions of the system performance are derived. The results
show that the system performance can be improved by increasing the transmit power at the relay. Increasing
the number of sensor nodes at the sensor cluster P helps improve its performance but causes a decrease in the
performance of the sensor cluster Q. The Monte Carlo simulation is conducted for validating the numerical
results. In future studies, we will expand the system model with multi-relay.
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[21] Duy-Hung Ha, Dac-Binh Ha, Jaroslav Zdralek, Miroslav Voznak. "Performance Analysis of Hybrid Energy
Harvesting AF Relaying Networks over Nakagami-m Fading Channels", 2018 International Conference on
Advanced Technologies for Communications (ATC), 2018.
[22] "Industrial Networks and Intelligent Systems", Springer Science and Business Media LLC, 2019.
[23] Van-Vinh Nguyen, Trong-Tuyen Tran, Vo Viet Tri, Van-Van Huynh, Hoang-Sy Nguyen, Miroslav Voznak.
"Power-Splitting Protocol Non-Orthogonal Multiple Access (NOMA) in 5G Systems", Proceedings of the Tenth
International Symposium on Information and Communication Technology - SoICT 2019, 2019.
[24] Dac-Binh Ha, Jai P. Agrawal. "Chapter 6 Performance Analysis for NOMA Relaying System in Next-Generation
Networks with RF Energy Harvesting", Intech Open, 2020.
APPENDIX
APPENDIX A: Proof of Theorem 1.
Here, the expression of Pout for SNP* can be formulated as
 
   
 
 
 
 
 
 
1 1 3 1 3
2 2 3 3 1 1 2 2
1 1 2 2 3 3 1 1 2 2
1 1 2 2
1 1 2 2 3 1 1 2 2
1 1 2 2 3
1 1
3
2 2
Pr
1 1
Pr 1 1
1
Pr 1 0 Pr , 1 0
1
Pr Pr
1
s
out t
t t
t
t t
t
t
t
Y X
P
X X Y X
Y X X Y X
Y X
Y X X Y X
Y X
Y
X
X
 

   
      
  
     
   




 
 
 
 
   
 
       
 
 
 
 
        
 
   
 
 
 
 
   
 

 
1 1
2 2 1 1
2 2
1 1
3
2 2
1
1
,
1
1
t
t
Y
X Y
X
Y
X

 



 

 
 

 
 

 
  
 

 
 

 
 

 
 
 
 
3 3 1 1
1
( ) 1 1 2 2
2
1 1 1 2 2
1 1 2 2
1
1 ( 1)
t
t
t
u i
N u
a
u
i
i
N
i e e du
i i u
i u

    

  
  
  

  


 
 
   
 
 


 
   
 
 
 
   
 
3 3 1 1
1 1 3 3
1 ( )
1 1 2 2
2
1 1 1 2 2
0 1 1 2 2
1
1 1 2 2 1 1 2 2 2 2
1 1 1 2 2
1
1 ( 1)
ln ln ln
1 ( 1)
ln
t t t
t
t i t
N
t
i
i t
t
i
N
b
t
i
i
N
i e e dt
i i t
i t
N z z i z i
ie
i z i
  
   

   
  
   
   
         

   
  
  

 
 
 
 
 

 
 
 
   
 
 
 
 
   
  
 
  
 

 
 

 
1 1
1 1 3 3
1 1
1
ln
2
0 2 2
1
1 1 2 2 1 1 2 2 2 2
2
1 1
1 1 2 2 2 2
ln ln
1 1 1
ln ln ln
2 2 2
1 ( 1)
2 1 1 1
ln ln ln
2 2 2
t
i
z
t
i i
l l l
N L
c t
i
i l l l l
t
e dz
z i z
a a a
i i
N ie
e
i L a a a
i i

 


   
 
  
         

      
 
 
 
 
 
 

 
  
 
  
   
  
   
  
 
 
 
 
 
 
 

 
1
ln
2
2
1 .
l
a
l
a



 ISSN: 2502-4752
894
APPENDIX B: Proof of Theorem 2.
Here, the expression of Pout for SNQ can be calculated by
 
 
   
 
2
2 3 2 1
2
1
3 3 2 2 1
2 3 2 3
1 1 2 2 3 3
1 2
2 3 2
0
1 ( 1)
1
0 1 2
0 0
Pr
1
1
1
1 ( 1)
1
1 ( 1
t
t t t
s
out t
t
t
X X X Y
t
y t t i y
t
N
i
i
X X
P
Y X X
y x
F F f x dyf y dx
x
N e
dydt
i
N
i


   
    
 

  
  

   
 
 
    
  
 


 
 
 
  
 
 
 
 
   
   

   

 
  
 
 

 
  
 
 
 
 
 
 
1
3 3 2 2
3 3 1
2 2 3 3 3 3 2 2
1
1
1
0 1 2
0 0
1
1 1
1
3 3
0 2 1 1 3 3
0
1
1
0
)
1
1 ( 1)
[ ( 1) ]
1
1 ( 1)
t t t
t
t t
t
t t
i
t
N y
t
i
i
t
N
t
i
i t
N
i
i
e
e dydt
N t
e e dt
i i t
N
e
i
  
 
   
  
 

       

 
 
    
  
   

 
  
 
 
 

  

    
 
 
 

 


 
  
 
 
 

 
  
 
 
  
 
 2 2 3 3 2 2
1 1 2
ln
3 3
2
2 1 1 3 3
0 [ ln ( 1) ]ln
t
z
t
e dz
z i z


    
  
     
 

 
 
  
 

2 2
2 2 3 3
1
ln
1 1 2
2 2
1
3 3
2
0 1
2 2
1 1 3 3
1 1
1 ( 1) .
1 1
2
ln ln ( 1)
2 2
l
t
a
N L
l
i
i l l l
t
N e a
N
e
a a
i
L
i

 

   
  
 
    

 

 
 
 
 
 
 
 
  
   
 
   
 
 
 

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