https://doi.org/10.31449/inf.v47i3.4145                                    Informatica 47 (2023) 383–392  383 
 
Computational Analysis of Uplink NOMA and OMA for 5G Applications: 
An Optimized Network 
 
Shelesh Krishna Saraswat, Vinay Kumar Deolia, Aasheesh Shukla 
1
Department of Electronics and Communication Engineering, GLA University, Mathura (281406), UP, India. 
E-mail: shelesh.saraswat@gla.ac.in, vinaykumar.deolia@gla.ac.in, aasheesh.shukla@gla.ac.in 
Keywords: Non-orthogonal Multiple Access (NOMA), Multiple orthogonal Access (MOA), Jain index 
        In this paper, the non-orthogonal multiple access (NOMA) schemes are compared with the multiple 
orthogonal access (OMA) schemes on the basis of the resource allocation validity of uplinks. By 
reflecting the involvement of a measure of each user’s data on the system’s total amount, we analyze 
the main reasons why NOMA provides justice service distribution over OMA on unequal channels. 
Moreover, the Jain index is observed and proposed to quantify the irregularity of numerous user 
channels, according to the metric for the Jain index based on the Jain index. More importantly, the 
proposed metric establishes the criteria for choosing between NOMA and OMA to share resources 
correctly. Based on this debate, we offer a program that combines NOMA and OMA to increase user 
integrity. Imitation effects substantiate the exactness of the proposed matrix and display improvement 
of the accuracy of the showcased NOMA-OMA mixture system as compared to standard OMA as well 
as NOMA systems. The Biggest technology development in the next years is the Internet of Things, 
which promises omnipresent connectivity of everything everywhere.  it's anticipated that over 25 
billion gadgets will be linked to cellular networks. Various challenges are faced by the wireless 
networks of Fifth generation (5G) , the main challenge discussed  in this paper regarding channel 
fetching schemes. For massive connectivity so that we can increase data rate and save bandwidth 
also. 
Povzetek: V tem članku so primerjane sheme neortogonalnega (NOMA) in ortogonalnega dostopa 
(OMA) glede na veljavnost dodeljevanja virov pri povezavah. 
 
1   Introduction& literature studies 
For 5th generation (5G) wireless networks, the non-
orthogonal multiple access (NOMA) schemewas 
identified as a favourablereflection of a multi-access 
system welcoming extra user and enhancing 
efficiency in spectral manner [1] - [5]. In the first 
version of this type of technique, the multiple user’s 
superposition transmission system (MUST), 
wasshowed for a 3-year partnership project advanced 
evolution networks i.e., 3GPP-LTE-A [6]. The basic 
impression of this technique is to take advantage in 
the field of power in order to cultivate two things: first 
thing should be used in multiple user multiplexing and 
the second thing to employ user intervention (SIC) for 
persistent disturbance (IUI) cancellation. In contrast to 
standard schemes for orthogonal multiple access 
(OMA) [7],[8], NOMA enables multiple 
transmissions simultaneously Superposition coding 
with varying power levels allows users to have the 
same degree of freedom (DOF). Meanwhile, through 
exploitation, advanced signal processing methods, 
such as SIC, can compensate for the received power 
differential to obtain the appropriate signals to the 
recipient. Compared to traditional OMA systems, 
NOMA has  
 
 
been shown to significantly increase the system’s 
spectral efficiency [9] - [11]. Consequently, NOMA I 
can support large connections, minimize latency in 
communication, and increase efficiency of the system 
by spectral means. Most current activities reduce 
NOMA programs [9] - [12]. 
On the other hand, NOMA is found in abundance in 
the uplink communication, where the electric waves 
are naturally placed at the top of the various forces 
received at the reception base station (BS). Aside 
from that, SIC recordings are usually more 
economical for BSs than they are for mobile 
customers. In [13], the authors compare NOMA with 
OMA regarding spectral energy point efficiency in the 
uplink. Lately, the researchers of [14], [15] have 
devised a resource allocation-based distribution 
technique for most instances (ML) recipients in BS. 
On the other side, one of the essential aspects of 
NOMA is that it ensures equity in resource 
distribution. Unlike OMA systems, which allow 
customers with bad channel conditions to be halted on 
service, NOMAlets users with varied channel settings 
to be served concurrently. In Ku [16], the NOMA 
uplink system presented a power allocation 
mechanism to provide users with max-min justice. Ku 
[17] looked into editing with a system of non-
orthogonal repetitious users who were inaccurate. In 

384 Informatica 47 (2023) 383–392    S.K. Saraswat   et al. 
 
[18], [19], investigative power distribution was 
investigated on one side, and many NOMA sticks 
below systems, respectively. Even though, the 
fairness was considered by the resource allocation-
based distribution technique [16] - [21], it was still 
uncertainabout NOMA to offer more resource 
allocation as compared to OMA. 
This paper wants to see how NOMA and OMA 
compare service accuracy uplink sharing. A selection 
condition is proposed when delivering status 
information for the current channel to determine if 
NOMA or OMA should be employed. By presenting 
the sacrifice of perfection individual user data rate 
system rating, we state why NOMA is fair to them 
service distribution than OMA on unequal channels. 
In addition, we offer a measure for the accurate 
indication of a closed-form for deciding when NOMA 
is superior to OMA for two users of the NOMA1 
system. Plus, a simple hybrid NOMA-OMA program 
that selects NOMA flexibly once The OMA in terms 
of the proposed metrics are proposed to continue 
improving user integrity. The numerical results are 
displayed to verify our proposed matrix’s accuracy 
and improve the merits of the proposed NOMAOMA 
integrated system. Some negatives are such as 
Overload and Preamble Collision Problems, 
Excessive Overhead , more QoS Requirements, Power 
radiations.The following is the order in which the 
paper is organized. The NOMA system uplink system 
and the chat NOMA and OMA capacity areas were 
presented in section II. The reason why NOMA is just 
more efficient than OMA is analyzed in section III. 
Alternatively, the metric indicator for the accuracy of 
the closed-form and the hybrid NOMA-OMA is a 
program that has been recommended. In Section IV, 
simulation effects are introduced and investigated. 
Finally, this paper is concluded in section V. 
 
Figure 1: An uplink of NOMA model with a base 
station and K users. 
The symbols used in this paper are as follows. 
Circular Gaussian distribution of the same complex 
with the mean 𝜇 and variance 𝜎 2
 is defined as 𝒞𝒩 (𝜇 , 
𝜎 2
); ∼ it must be “distributed as “; ℂ stands for a 
collection of all the complex numbers; ∣⋅∣ describes 
the total amount of the complex scalar; Pr {⋅} mean 
chances of random occurrence. Smart indoor 
communications, remote area communication, smart 
outdoor communication for smart city Auto-pilot 
UAVs, Self-driving Electrical Vehicles, Fast Regional 
Trains are some relevant examples.  
 
2   Model of noma and oma system 
The NOMA model for uplink is introduced in this part 
along with the NOMA, OMA power zones. 
A. System Model 
As indicated in Figure, we’re putting the NOMA 
uplink technology to the test with single-antenna BS 
and 𝐾 users.All 𝐾 users send within one network 
companywith the same transmission power (𝑃 0
). For 
the NOMAsystem, 𝐾 no. of users is repeated in the 
identical network company with dissimilar power 
levels are not accepted. In contrast, in the OMA 
system, 𝐾 users use a network company that uses a 
time-sharing strategy [22]. The signal received on BS 
is given by the NOMA system. 
 
𝑦 = ∑
√𝑝 𝑘 ℎ
𝑘 𝑠 𝑘 + 𝑣 𝐾 𝑘 =1
   (1) 
 
where ℎ
𝑘 ∈ ℂis defined as the channel 
coefficientsbetween BS and the user, and 𝑘 =
{1, … … , 𝐾 } is the channel 
coefficient between BS and the user, 𝑠 𝑘 defines a 
modified symbol to user 𝑘 , 𝑝 𝑘 means user transfer 𝑘 , 
and 𝑣 ∼𝒞𝒩 (0, 𝜎 2
) means an additional white 
Gaussian sound (AWGN) in BS and 𝜎 2
 sound power. 
Without losing common sense, we think |ℎ
1
|
2
≤
|ℎ
2
|
2
≤ ⋯ |ℎ
𝐾 |
2
. 
B. Region of Power 
The OMA system is well-known for optimal DOF 
allocation and multimedia NOMA application. As 
demonstrated in Figure 2, the power supply can reach 
the same quantity of system uplink transmission [22], 
[23]. Here is the full-service offer for either NOMA 
and OMA techniques. 
OMACapacity region of 
NOMAOptimal point for 
OMAOptimalpointforNO
MA Capacity region of 
OMACapacity region of 
NOMAOptimal point for 
OMAOptimalpointforNO
MA 

Computational Analysis of Uplink NOMA and OMA for 5G…                                     Informatica 47 (2023) 383–392   385 
 
 
Figure 2: The capacity region of NOMA and OMA 
for realization of a single channel with one BS plus 
two users. 
The capacity region having NOMA as well OMA for 
a single channel realization with a BS plus two users 
illustrated in Fig. 2. The two users have transmitted 
power of 𝑃 0
= 20 𝑑𝐵 . When the curve of |
ℎ
1
ℎ
2
|
2
= 1, 
we have 
|ℎ
1
|
2
𝜎 2
=
|ℎ
2
|
2
𝜎 2
= 20 𝑑𝐵 . For the curve of 
|
ℎ
1
ℎ
2
|
2
= 10, we have 
|ℎ
1
|
2
𝜎 2
= 18 𝑑𝐵 , and 
|ℎ
2
|
2
𝜎 2
=
28 𝑑𝐵 . 
𝛼 𝑘 =
|ℎ
𝑘 |
2
∑ |ℎ
𝑖 |
2 𝐾 𝑖 =1
; ∀𝑘   (2) 
It is noted that 𝛼 𝑘 can be translated to the as a normal 
channel benefit 𝑘 . In other terms, a fair share of DOF 
through the OMA program to share network company 
with duration in proportion to their normal channel 
benefits, and depends on the distribution of time 
variables according to immediate channel fulfillment. 
We recognize that it is correct The DOF distribution is 
available to all users who submit via their 
transmission capacity is 𝑃 0
 as there is no IUI in its 
OMA system. 
Furthermore, by considering 𝑝 𝑘 = 𝑃 0
, ∀𝑘 , and 
executing SIC in BS [22], [24], an effective NOMA 
power distribution corner points, namely point A, B, 
D, and E in the middle Fig. 2, may be found. A time-
sharing approach can be used to gain any pair of 
scales in line segments between existing locations. 
Even though OMA’s regional capacity is lower than 
NOMA’s, Fig. 2 indicates that NOMA regularly beats 
OMA on the basis of spectral efficiency and its user 
bias, thanks to its time-sharing mechanism. We 
should emphasize that NOMA can only obtain corner 
points in the power field without a time-sharing 
method, resulting in less fairness than OMA in 
instances. 
This paper showcase about the legitimacy of NOMA 
usersystems with absence of time-sharing and the 
OMA system with flexible DOF distribution. Both 
methods receive the similar system sum-rate but lead 
to unlike users’ justice. With understanding, in Fig. 2, 
of a channel equal to |
ℎ
1
ℎ
2
|
2
= 1, OMA in area C is 
better than NOMA as both the users have the similar 
amount of specific data. However, with channel of 
asymmetric having|
ℎ
1
ℎ
2
|
2
= 10, It should be observed 
that OR in point D is preferable to OMA in the right 
place F. Consequently, it is fascinating to present 
explanations for justice development of NOMA on 
unequal channels and availability of a quantifiable 
fairness indicator to determine the superiority of 
NOMA overOMA. 
3   Fairness comparison between 
noma and oma 
In this section of the paper, the Jain justice accepted 
index has been introduced [25] for assessing resource-
based allocation goodness. Then, overall rating 
towards contribution of each and every user data rate 
in the systems have been presented. The main reasons 
why NOMA is less biased than OMA. The closed 
version of the justice index in the NOMA system for 
two users is based on the Jain index [25] to govern if 
you are utilizing either NOMA or OMA in 
combination of users in a single network firm. In 
addition, a proposed NOMA or hybrid system using 
NOMA or OMA is proposed flexibly based on the 
showcased matrix. Because of NOMA technique. 
Internet speed of communication could be better 
which can increase the visibility of E-Commerce. 
A. Jain’s Fairness Index  
In this study, Jain’s index is used [25] to quantify 
fairness in the subsequent scenarios. 
𝐽 =
( ∑ 𝑅 𝑘 𝐾 𝑘 =1
)
2
𝐾 ∑ ( 𝑅 𝑘 )
2 𝐾 𝑘 =1
    (3) 
Where𝑅 𝑘 refers to each user level 𝑘 . Note that 
1
𝐾 ≤
𝐽 ≤ 1. A system with a greater Jain index is very good 
and reaches the extreme when each and very users 
receive the same amount of specific data. 
B. Analyzing Righteousness 
For the full-service offer, both NOMA as well as 
OMA programs, deliberated in part II-B, are readily 
availablethe total amount and data levels for each of 
the two schemes as follows: 

386 Informatica 47 (2023) 383–392    S.K. Saraswat   et al. 
 
 
 
𝑅 𝑠𝑢𝑚 𝑁𝑂𝑀𝐴 = 𝑅 𝑠𝑢𝑚 𝑂𝑀𝐴 = ∑ 𝑅 𝑘 𝑁𝑂𝑀𝐴 𝐾 𝑖 =1
= ∑ 𝑅 𝑘 𝑂𝑀𝐴 𝐾 𝑖 =1
= log
2
( 1 +
𝑃 0
𝜎 2
∑ |ℎ
𝑖 |
2 𝐾 𝑖 =1
)   (4) 
𝑅 𝑘 𝑁𝑂𝑀𝐴 = log
2
( 1 +
𝑃 0
|ℎ
𝑘 |
2
𝑃 0
∑ |ℎ
𝑖 |
2 𝑘 −1
𝑖 =1
+𝜎 2
)                                                                          (5) 
𝑅 𝑘 𝑂𝑀𝐴 = 𝛼 𝑘 𝑅 𝑠𝑢𝑚 𝑂𝑀𝐴                                                                             (6) 
 
Wherethe entire program rating of NOMA and OMA 
schemes with suitable resource allocation is referred 
to as 𝑅 𝑠𝑢𝑚 𝑁𝑂𝑀𝐴 and 𝑅 𝑠𝑢𝑚 𝑂𝑀𝐴 ,𝑅 𝑘 𝑁𝑂𝑀𝐴  and 𝑅 𝑘 𝑂𝑀𝐴   in NOMA 
and OMA applications, respectively, refer to a 
measure of user data. We first define the collection of 
normal channel gain in the NOMA program, such 
as∅
𝑘 = ∑ 𝛼 𝑖 𝑘 𝑖 =1
, 𝑘 = {1, … , 𝐾 },∅
0
= 0, then rewrite 
the user-accessible number 𝑘 as 
𝑅 𝑘 𝑁𝑂𝑀𝐴 = log
2
( 1 +
𝑃 0
∅
𝑘 𝜎 2
∑ |ℎ
𝑖 |
2 𝐾 𝑖 =1
)− log
2
( 1 +
𝑃 0
∅
𝑘 −1
𝜎 2
∑ |ℎ
𝑖 |
2 𝐾 𝑖 =1
)    (7) 
 
Figure 3: Diagram of the total level of the system 
against accumulative normalized NOMA, OMA 
channel benefits with 𝐾 = 5users. The green double. 
arrow shows total NOMA program and OMA 
program values . The NOMA and OMA program’s 
ratings are depicted by red and black line segments, 
respectively. 
The first term (7) denotes the system’s total value of 
users, and the second term refers to a system 
having𝑘 − 1number of users. To put it another way, 
the role of user k to overall system rating is 
determined by the logarithm function difference 
concerning ∅
𝑘 and ∅
𝑘 −1
. We explain the logarithm 
function as follows for the sake of simplicity and 
generality. 
𝑔 ( 𝑥 )= log
2
( 1 + 𝛤𝑥 ) ;  0 ≤ 𝑥 ≤ 1                                                                          (8) 
with 
𝛤 =
𝑃 0
𝜎 2
∑|ℎ
𝑖 |
2
𝐾 𝑖 =1
 ; 
𝑅 𝑘 𝑁𝑂𝑀𝐴 = 𝑔 ( ∅
𝑘 )− 𝑔 ( ∅
𝑘 −1
)  (9) 
Furthermore, OMA program, it is seenfrom (6) to 
𝑅 𝑘 𝑂𝑀𝐴 has a line in association with 𝑅 𝑠𝑢𝑚 𝑂𝑀𝐴 and the 
slope w.r.t. the total amount of the system is 
determined by normal channel gain 𝛼 𝑘 = ∅
𝑘 − ∅
𝑘 −1
. 
Similarly, the difference between the line functions 
∅
𝑘 ,∅
𝑘 −1
 determines the user contribution k in overall 
system rating, where 
𝑓 ( 𝑥 )= log
2
( 1 + 𝛤 ) 𝑥 ; 0 ≤ 𝑥 ≤ 1 ; and 
𝑅 𝑘 𝑂𝑀𝐴 = 𝑓 ( ∅
𝑘 )− 𝑓 ( ∅
𝑘 −1
)  (10) 
Fig. 3 depicts the linear as well as logarithmic rise in 
model data rate in OMA and NOMA with 𝐾 =
5 uplink users as a function of accumulated channel 
profits. It is noteworthy that NOMA and OMA 
programs have four the total amount of the same 
system but given a different date individual user 
number. In particular, the NOMA system gains a 
better service share than the OMA system because all 
users are assigned the same person’s prices. The 
logarithmic map of 𝑔 ( ∅
𝑘 ) concerning Accumulated 
channel gain ∅
𝑘 , in reality, helps the fairness of 
service sharing in NOMA. The first and second 
outputs, respectively, of which 𝑔 ( ∅
𝑘 )  increases and 
decreases concerning ∅
𝑘 . Large uza normal channel 
gain kuhamba, slow 𝑔 ( ∅
𝑘 ) increase by ∅
𝑘 , When 
compared to the OMA method, this results in a 
modest sum per individual. On the other hand, a small 
normal gain of the channel 𝛼 𝑘 can lead to an increase 

Computational Analysis of Uplink NOMA and OMA for 5G…                                     Informatica 47 (2023) 383–392   387 
 
increasing level of 𝑔 ( ∅
𝑘 ) by ∅
𝑘 , the higher a person 
the rate is attainedrelated to that of the OMA system. 
Because for example, it is considered a weak user and 
a very strong user with standard channel gain of 𝛼 1
 
and 𝛼 𝐾 , respectively, 𝑅 1
𝑁𝑂𝑀𝐴 suggested logarithm 
function 𝑔 ( 𝑥 ) in comparison to 𝑅 1
𝑁𝑂𝑀𝐴 , when 
compared to 𝑅 𝐾 𝑂𝑀𝐴 , 𝑅 𝐾 𝑁𝑂𝑀𝐴 is lower. 
Note 1: It’s worth noting that OMA line mapping is 
more straightforward than the NOMA program for 
equal channels. However, the chances are that all 
users are the same. The benefits of the channel are 
very small, particularly for a program with a huge 
user base 
C. Metric for Fairness Indicator 
In reality, most NOMA programs believe at least two 
users repeat with the similar DOF [11, 12, 26], which 
can minimize both computational difficulty and 
recipient coding latency. As a result, in this portion, 
we concentrate on a fair comparison of NOMA as 
well as OMA with   𝐾 = 2. We’d like to construct 
simple criteria for determining whether NOMA is 
significantly better than OMA for two users, for 
which it is crucial for improving user planning in a 
system having multiple DOFs and users. The 
following theorem proposes righteousness as a metric 
index. Theory 1: If fewer users are provided with 
channel |ℎ
2
|
2
≥ |ℎ
1
|
2
, the NOMA system is really 
fair to a strong logic of Jain’s right to righteousness 
only if. 
|ℎ
1
|
2
|ℎ
2
|
2
≤
𝛽 1−𝛽    (11) 
 where  
𝛽 =
𝑊 (
( 1+𝛤 )
1+
1
𝛤 log( 1+𝛤 )
𝛤 )
log ( 1+𝛤 )
−
1
𝛤 and 𝑊 ( 𝑥 ) : the Lambert 
𝑊 function. For high SNR regime, i.e., 𝛤 → ∞, We 
have an approximation of 𝛽 as with a high SNR. 
𝛽 ̃
=
𝑊 ( log ( 1+𝛤 ) )
log ( 1+𝛤 )
    (12) 
Proof: Because the sum of the NOMA and OMA 
schemes is the same, we must compare the total 
square of individual ratings (SSR), that is, 𝑆𝑆𝑅 =
∑ ( 𝑅 𝑘 )
2 2
𝑘 =1
, the denominator value of the NOMA 
scheme (3). A system with a modest SSR can be 
skewed in Jain's way. Through the OMA program, 
Sine 
𝑆𝑆𝑅
𝑂𝑀𝐴 = ( log
2
( 1 + 𝛤 ) )
2
( 𝛼 1
2
+ 𝛼 2
2
) 
= ( log
2
( 1 + 𝛤 ) )
2
( 1 + 2𝛼 1
2
− 2𝛼 1
) (13) 
where 0 ≤ 𝛼 1
≤ 0.5 since we assume |ℎ
1
|
2
≤ |ℎ
2
|
2
. 
The 𝑆𝑆𝑅
𝑁𝑂𝑀𝐴 can be used by, in the NOMA scheme- 
𝑆𝑆𝑅
𝑁𝑂𝑀𝐴 = ( log
2
( 1 + 𝛤 𝛼 1
) )
2
+ ( log
2
( 1 + 𝛤 )− log
2
( 1 + 𝛤 𝛼 1
) )
2
 
= ( log
2
( 1 + 𝛤 ) )
2
+ 2 log
2
( 1 + 𝛤 𝛼 1
) )
2
−
2 log
2
( 1 + 𝛤 )log
2
( 1 + 𝛤 𝛼 1
)  
 (14) 
It’s worth noting that the smaller SSROMA solution = 
SSRNOMA has 𝛼 1
= 0, which corresponds to a 
single user situation. Moreover, in 𝛼 1
= 0.5, that 
is,|ℎ
1
|
2
= |ℎ
2
|
2
, we have𝑆𝑆𝑅
𝑂𝑀𝐴 < 𝑆𝑆𝑅
𝑁𝑂𝑀𝐴 as 
observed in the volume region of Figure 2. In 
addition, 𝑆𝑆𝑅
𝑂𝑀𝐴 is a monotonic entity decrease 
between 0 ≤ 𝛼 1
≤ 0.5, while 𝑆𝑆𝑅
𝑁𝑂𝑀𝐴 is a 
monotonic degradation function of 𝛼 1
 within 0 ≤
𝛼 1
≤
√1+𝛤 −1
𝛤 and increases by 𝛼 1
 within 
√1+𝛤 −1
𝛤 ≤
𝛼 1
≤ 0.5. And, from Figure 2, we can witness that 
𝑆𝑆𝑅
𝑂𝑀𝐴 > 𝑆𝑆𝑅
𝑂𝑀𝐴 of small positive negligence 𝛼 1
. 
Therefore, there is a unique combination of 𝑆𝑆𝑅
𝑂𝑀𝐴 
and 𝑆𝑆𝑅
𝑁𝑂𝑀𝐴 at 𝛼 1
= 𝛽 in the range 0 ≤ 𝛼 1
≤ 0.5. 
Before intersection, i.e., 0 < 𝛼 1
< 𝛽 , NOMA is best, 
after that at a crossroads, i.e., 𝛽 < 𝛼 1
< 0.5, OMA is 
very good. Solving 𝑆𝑆𝑅
𝑂𝑀𝐴 = 𝑆𝑆𝑅
𝑁𝑂𝑀𝐴 within 0 ≤
𝛼 1
≤ 0.5, we find 
𝛽 =
𝑊 (
( 1+𝛤 )
1+
1
𝛤 log ( 1+𝛤 )
𝛤 )
log ( 1 + 𝛤 )
−
1
𝛤 
Moreover, at 𝛼 1
≤ 𝛽 , we have 
|ℎ
1
|
2
|ℎ
2
|
2
≤
𝛽 1−𝛽 , i.e., 
completes the proof of adequacy of the proposed 
fairness metric indicator. As needed, the region 
between  0 < 𝛼 1
< 0.5 where 𝑆𝑆𝑅
𝑂𝑀𝐴 > 𝑆𝑆𝑅
𝑁𝑂𝑀𝐴 is 
0 < 𝛼 1
< 𝛽 is different, which is the sole region 
between  0 < 𝛼 1
< 0.5. In other sense, NOMA is 
only useful if 0 < 𝛼 1
< 𝛽 is true, demonstrating the 
requirement for this suggested metric. 
Note 2: It should be noted that the proposed accuracy 
index is exclusively dependent on the parameter 
𝛤 described in (9). As an outcome, the statistic is 
focused on the immediate earnings of the channel. In 
comparison to the Jains, our proposed metrics, which 
comprise OMA and NOMA, provide greater insight. 
Especially at the top SNR limitations (12), we can see 
that 𝛽 ̃
 decreases by a significant increase in 
transmission power from Lambert. The W function in 
number rises slightly higher than that of the 𝑊 
denominator. Therefore, the chances of NOMA being 
the best will fall when the top post is promoted power, 
which will be guaranteed in imitation. 

388 Informatica 47 (2023) 383–392    S.K. Saraswat   et al. 
 
 
Figure 4: The probability of NOMA being fairer than 
OMA versus the maximum transmit power, 𝑃 0. D. 
Hybrid OR OMA Scheme. 
Theorem 1 presents a metaphor for the accuracy index 
that simplifies assessing if NOMA is superior to 
OMA and will serve as a condition of the user’s 
schedule design systems that have multiple network 
companies that provide multiple users, In particular, 
with the wrong user planning strategy, we suggest a 
flexible mix scheme that determines each pair users in 
each network company in the selection or OMA 
system or a NOMA program to improve user 
integrity. Instead of using the NOMA program or 
OMA system in all areas with fewer carriers, this 
NOMA-OMA mixed program can improve I very 
user compatibility. It should be noted that it can be 
continuously enhanced if fairness is developed in 
conjunction with user editing. Future efforts will be 
considered. 
4   Simulation results 
We use simulation in this section to ensure that the 
suggested matrix and test hybrid OR OMA method 
are both effective. One cell with BS available center 
with cell radius 400 m is taken into consideration. 
There are𝑁 𝐹 = 128 sub-system carriers,2𝑁 𝐹 users are 
arbitrarilypaired across all subcarriers. In a cell, all 
2𝑁 𝐹 users are dispersed at random and uniform 
manner. Under BS, we set the volume of each carrier 
to 𝜎 2
= −90dBm. 3GPP path loss model in the form 
of large urban cellsaccepted into our estimates [27]. 
Figure 4 shows the potential for OR greater is better 
than OMA compared to high transmission capacity, 
𝑃 0
. It is worth noting that Pr {
|ℎ
1
|
2
|ℎ
2
|
2
≤
𝛽 1−𝛽 } fits well 
with 𝑃𝑟 {𝐽 𝑁𝑂𝑀𝐴 ≥ 𝐽 𝑂𝑀𝐴 }. Simply put, our proposed 
goodness meter index can guess if NOMA is quite 
accurate as compared to OMA. Also, with increased 
SNR,𝛽 ̃
 values in equation (12) wherePr {
|ℎ
1
|
2
|ℎ
2
|
2
≤
𝛽 ̃
1−𝛽 ̃
}strictly related to imitation effects. Furthermore, 
according to the Jain index, the NOMA scheme has a 
higher likelihood of better justice (0.75~ 0.8) than the 
OMA model. This is because of the fact that the 
chances of the channels are asymmetric, much largest 
than the equivalent channels. Furthermore, high 
transfer power reduces the odds of NOMA being 
biased, as stated in Remark 2: When contrasted to a 
high-transfer-power system, this is owing to the 
NOMA system’s limited interference. They are 
powerless because the user (with high acquired 
power) will be subjected to a significant level of 
distraction, whilst SIC will not influence the weak 
user (poor power is not accepted) recording. As a 
result, in high transfer capacity, the weak user can 
Achieve a considerably greater data rate than a strong 
user, which is possible the result of slightly better 
service delivery than OMA. However, NOMA is still 
better than OMA possibly about 0.75 in the maximum 
transmission system. Figure 5 shows the potential for 
congestion work (PDF) of the user rating of the 
company’s network system with random pairing. The 
NOMA, OMA as well as the hybrid NOMA-OMA 
systems (which is proposed in this paper) are all 
comparable multiple access strategies. Apparently, the 
average data distribution of each NOMA model is 
higher and more focused as compared to the OMA 
model, i.e. The NOMA system provides a more 
equitable resource-based allocation than OMA 
system. In general, the distribution of each level of the 
combined NOMA-OMA hybrid system is 
quiteintensive than its counterpart i.e., NOMA 
system. In actuality, the combination we’ve proposed 
is a bit of a mishmash. Based on the metric index of 
accuracy, you can switch between NOMA, OMA as 
well as the NOMA-OMA program. It can make better 
use of the channel’s benefits connection. The 
values𝐽 𝑁𝑂𝑀𝐴 = 0.76, 𝐽 𝑂𝑀𝐴 = 0.62 is taken into 
consideration, whereas the value of Jain index is 
considered as 𝐽 𝐻𝑦𝑏𝑟𝑖𝑑 = 0.91 for NOMA-OMA 
hybrid model. The above-mentioned values are the 
results of three multiple access strategies. 

Computational Analysis of Uplink NOMA and OMA for 5G…                                     Informatica 47 (2023) 383–392   389 
 
 
Figure 5: The user rate’s PDF for NOMA, OMA, as 
well as Hybrid NOMA-OMA model 
 
Figure 6: The CDF of the NOMA scheme, the OMA 
scheme, and the hybrid NOMA-OMA scheme 
Furthermore, the user level increasing distribution 
function (CDF) is approx. It was fascinating in 
operation, as illustrated in Figure 6. Compared to the 
NOMA system, the 10-percentile user level, which 
has a tenuous connection to fairness and user 
experience, increases by around 1 bit/Hz/s. This 
suggests that our suggested NOMA-OMA hybrid 
method can provide significantly better performance 
for low-level users while also improving user data 
quality. With the aid of cmos, we may employ this 
method for radiation losses. Different modulation 
techniques may enable us to develop a more effective 
E-Commerce solution in the future. 
 
5   Conclusion 
The resource distribution inequalities between NOMA 
as well as OMA programs into the uplinks are 
examined in this study. By inserting the characters of 
the influence of each user’s data rate to the total 
system rating, the basic reason why NOMA is more 
suited than OMA in irregular multiple user channels 
was studied. A logarithmic map within the typical 
channel benefits and estimates of each data that 
utilizes the channel gains asymmetry is utilized to 
increase user integrity in NOMA system. On the basis 
of this remark, we have raised the value fairness 
indicator metric for NOMA systems for two users 
determines whether NOMA provides a more equitable 
service distribution than OMA. Furthermore, we 
offered a NOMA-OMA mix, which flexibly selects 
NOMA and OMA for user development goodness 
based on the provided technique. When NOMA is less 
biased than this OMA, our recommendation metric 
can reliably detect it. According to numerical results. 
Otherwise, a proposed mixed NOMA-OMA system 
can considerably increase user integrity compared to 
traditional NOMA-OMA schemes. The enormous 
economic advantages of mobile commerce are clear 
when 5G and A IoT technology are combined. The 
rapid rise of mobile commerce made possible by 5G's 
high speed, vast capacity, and low latency 
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