Ant-colony and nature-inspired heuristic models for NOMA systems: a review

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Vol. 18, No. 4, August 2020, pp. 1754~1761
ISSN: 1693-6930, accredited First Grade by Kemenristekdikti, Decree No: 21/E/KPT/2018
DOI: 10.12928/TELKOMNIKA.v18i4.14995  1754
Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA
Ant-colony and nature-inspired heuristic models
for NOMA systems: a review
Law Poh Liyn1
, Hadhrami Ab. Ghani2
, Farah Najwa Roslim3
, Nur Asyiqin Amir Hamzah4
,
Saeed Mohammed Abdulghani Mohammed5
, Nor Hidayati Abdul Aziz6
,
Azlan Abd. Aziz7
, Tan Kim Geok8
, Azizul Azizan9
1-8
Faculty of Engineering and Technology, Multimedia University, Malaysia
9
Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Malaysia
Article Info ABSTRACT
Article history:
Received Dec 15, 2019
Revised Mar 17, 2020
Accepted Apr 7, 2020
The increasing computational complexity in scheduling the large number of
users for non-orthogonal multiple access (NOMA) system and future cellular
networks lead to the need for scheduling models with relatively lower
computational complexity such as heuristic models. The main objective of this
paper is to conduct a concise study on ant-colony optimization (ACO) methods
and potential nature-inspired heuristic models for NOMA implementation in
future high-speed networks. The issues, challenges and future work of ACO
and other related heuristic models in NOMA are concisely reviewed. The
throughput result of the proposed ACO method is observed to be close to the
maximum theoretical value and stands 44% higher than that of the existing
method. This result demonstrates the effectiveness of ACO implementation
for NOMA user scheduling and grouping.
Keywords:
Ant-colony
Heuristic
Orthogonal
This is an open access article under the CC BY-SA license.
Corresponding Author:
Hadhrami Ab. Ghani,
Faculty of Engineering and Technology,
Multimedia University,
Ayer Keroh Lama St., 75450 Bukit Beruang, Melaka, Malaysia.
Email: hadhrami.abdghani@mmu.edu.my
1. INTRODUCTION
Orthogonal multiple access (OMA) is a technique which allows multiple users to access wireless
network bandwidth resources orthogonally such that the signal transmitted by the users will not interfere between
each other over the frequency domain. On the other hand, non-orthogonal multiple access (NOMA) [1–6] is an
alternative technique introduced to access wireless network bandwidth resources simultaneously whilst
maintaining as well as improving the achievable capacity when power allocation is performed accordingly. Unlike
OMA, NOMA promotes bandwidth sharing by multiple users to increase the bandwidth efficiency whilst
increasing the sum capacity by sharing the same frequency carrier among users [7–11]. Sharing of bandwidth
improves the spectral efficiency and more users can be accommodated at once. The capacity of the system can be
enhanced by implementing NOMA by means of reducing the interference caused by the sharing user(s) in
the same group via successive interference cancellation (SIC) [12–17]. Interference, without SIC, tend to grow
worse when many users access the system at the same time. Therefore, user scheduling and allocation of resources
along with SIC are necessary to reduce interference in the system which is caused by the sharing users.
The increasing number of users in cellular networks nowadays tends to level up the computational
complexity [18] in determining the best user pairs or groups for accessing the system when these users are
allocated with the same frequency bandwidth. While reducing the computational complexity, the achievable

1755TELKOMNIKA Telecommun Comput El Control 
Ant-colony and nature-inspired heuristic models for NOMA systems: a review (Law Poh Liyn)
throughput and spectral efficiency of NOMA should be maintained or improved to be better than OMA, which is
the prior access technique implemented in cellular networks. To tackle these issues, scheduling methods with
lower computational complexity including ACO-based schemes [19] and heuristic methods such as particle
swarm optimization (PSO) schemes, genetic algorithms [20] are proposed for cellular and wireless networks [21]
to improve the throughput, increase the spectral efficiency, and reduce the complexity and interference [22]. Other
scheduling schemes have also been proposed in literature to obtain the best user pair such as round robin (RR)
Scheduling but it has high computational complexity [18]. Hence, models with relatively lower computational
load requirements such as heuristic artificial intelligence models are useful to be considered and studied in this
paper in order to determine the user groups while improving the throughput and spectral efficiency as well as
reducing the required computational complexity. Therefore the main contributions of this paper are firstly to carry
out a concise study on the existing user scheduling schemes for NOMA based on ACO and other heuristic models
and secondly to propose an ACO-based scheme which is demonstrated to be a suitable candidate for NOMA
systems in achieving throughput and spectral efficiency with reduction in complexity.
2. LITERATURE REVIEW
NOMA is a technique which utilizes the radio access network (RAN) in order to provide radio
connection between the mobile terminals and the radio network. In order to improve the capacity and reduce
the latency, a suitable channel access method must be designed with the given available radio resources. As
the demand for the capacity improvement with latency reduction keeps increasing, NOMA systems which is also
offering higher bandwidth efficiency is a good candidate for achieving the target improvement. Various studies
show that NOMA can be a good choice for channel access mechanisms. There are a number of desirable benefits
from NOMA implementation including greater spectrum efficiency and sum capacity [23]. NOMA is generally
implemented either as a power-domain system or code-domain system. In the power-domain system, the signals
which come from multiple users are superposed to render a single resultant signal. This resultant signal is
transmitted over the same channel. At the receiver side, the signals are de-multiplexed and detected with the help
of multiuser detection (MUD) algorithms such as SIC [24]. Wireless transmission systems must accommodate an
ultra-dense network with high number of users as it needs a reliable and fast channel access technique. One of
the major challenges that will be faced by the system is the efficiency in terms of spectrum and energy. Due to
the large number of signal transmissions occurring at the same time, they tend to experience different channel
conditions and transmission requirements.
NOMA overcomes this problem by allocating the resources based on the quality of the user’s signal,
which is typically measured by the signal-to-noise ratio. In other words, NOMA makes use its features to expand
and contract the coverage of services based on the condition and demands from users all the time by operating at
the right point which balances the spectrum and energy efficiency [25]. Although NOMA has greater spectrum
efficiency, the system is exposed to interference from the sharing users [26]. Therefore, user scheduling is
essential for reducing the interference and enhancing the bandwidth efficiency along with the successive
interference cancellation, which will also contribute in eliminating the computational complexity of
the algorithms [27]. Several methods are proposed for the formation of user pairs such as round robin [28]. As
these approaches tend to require significantly higher computational loads when the number of users increases,
new computationally lower schemes have been proposed in literature to determine the user pairs such as
the heuristic models which are inspired by artificial intelligence approaches [29–34] which include drosophila
optimization algorithm [35], particle swarm optimization algorithm [36–38], firefly optimization algorithm [39],
dolphin echolocation algorithm [40], genetic algorithm [20, 41] and ant-colony optimization algorithm [19, 42].
These models have the ability to solve problems in varying fields such as, but not limited to, transportation, signal
processing, image processing and biomedical engineering. This paper is presented focusing on the heuristic
models which are inspired by natural phenomena.
3. NOMA SYSTEM MODEL
In this paper, the considered system model consists of a network having 𝑁s sites, each of which possesses
a single cell that contains three sectors, where each of the sectors is represented as 𝐴𝑖,𝑗 for 𝑖 ∈ [1, 𝑁s] and
𝑗 ∈ [1, 3]. Assuming that the first cell is located in the middle of the network, the analysis will be focused at this
first cell without loss of generality. It is also assumed that all sectors in all sites of the networks are fully loaded,
which means that the available frequency resources are fully utilized, uniformly and randomly, by all users in
the sectors. Therefore, the focus of our analysis will be the first sector, denoted as 𝐴1,1, of the first site located in
the center of the network. There are 𝑁𝑢 users uniformly and randomly distributed in this sector. The users in each
sector are allocated with 𝑁rb basic units of time-frequency resources known as physical resource blocks (RB) in
the network.

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For user 𝑢 in the 𝑗-th sector, 𝐴𝑖,𝑗, of site 𝑖, the average received power over RB 𝑟 is expressed as
follows [27]
𝑃𝑖,𝑗,𝑢,𝑟 = 𝑃𝑢 𝐺PL(𝑖, 𝑢)𝑐𝑖,𝑢 𝐺A(𝑖, 𝑗, 𝑢)𝑓𝑖,𝑗,𝑢,𝑟,
where 𝑃𝑢 = 𝛼 𝑢 𝑃T is the average transmit power, which is assumed to be equally allocated to each RB, for user
𝑢 over RB 𝑟 with 𝛼 𝑢 and 𝑃𝑇 are respectively the allocated power ratio and the total allocated power per RB.
The following parameters, 𝐺PL(𝑖, 𝑢) and 𝑐𝑖,𝑢, are the path gain and shadow fading between cell or site 𝑖 and
user 𝑢, assuming that the shadowing effects experienced by the network sites are fully correlated. The antenna
gain between sector 𝐴𝑖,𝑗 and user 𝑢 is expressed as 𝐺A(𝑖, 𝑗, 𝑢) and the small scale fast fading is represented as
𝑓𝑖,𝑗,𝑢,𝑟 over RB 𝑟 between sector 𝐴𝑖,𝑗 and user 𝑢. In a NOMA system generally implemented for user pairing,
two users are configured to share the same RB 𝑟. In order to perform the SIC, the signal to interference plus
noise ratio (SINR) of both users, user 𝑢1 and user 𝑢2 are measured for the case of no RB sharing which is
the orthogonal multiple access (OMA) model, where the SINR 𝛾𝑢,𝑟
1,1
(𝑢) for a user 𝑢 (which is applicable for
both user 𝑢1 and user 𝑢2) at sector 1 of site 1 is written as follows
𝛾𝑢,𝑟
1,1
(𝑢) =
𝑃1,1,𝑢,𝑟
∑ ∑ 𝑃𝑖,𝑗,𝑢,𝑟
3
𝑗=1
𝑁s
𝑖=1 − 𝑃1,1,𝑢,𝑟
, (1)
The noise expression can be excluded in the above equation as the considered system model is
interference limited. If the SINR of user 𝑢1 is larger than the SINR of user 𝑢2 ie. 𝛾𝑢1,𝑟
1,1
(𝑢1) > 𝛾𝑢2,𝑟
1,1
(𝑢2) based
on (1), then the average power allocation for both user 𝑢1 and user 𝑢2 in NOMA is 𝛼1 𝑃T and 𝛼2 𝑃T respectively
as both users share the same RB 𝑟, where 𝛼1 < 0.5 and 𝛼1 + 𝛼2 = 1. Therefore, the SINR 𝛾𝑢2,𝑟
1,1
(𝑢1, 𝑢2) for
user 𝑢2 in sector 1 of site 1 can be expressed as follows
𝛾𝑢2,𝑟
1,1
(𝑢1, 𝑢2) =
𝑃1,1,𝑢2,𝑟
∑ ∑ 𝑃𝑖,𝑗,𝑢2,𝑟
3
𝑗=1
𝑁s
𝑖=1 − 𝑃1,1,𝑢2,𝑟 + 𝑃1,1,𝑢1,𝑟
, (2)
The SINR of user 𝑢2 in (2) is determined directly without performing the SIC. In other words,
decoding the received signal for user 𝑢2 is performed directly without any prior SIC operation. Therefore, the
interference caused by user 𝑢1 must be included in the calculation of SINR of user 𝑢2. In order to determine
the SINR of user 𝑢1 hence decoding the signal received for user 𝑢1, the SIC operation is performed in order to
remove the interference caused by user 𝑢2. Therefore, no interference caused by user 𝑢2 will be included in
the expression of the SINR 𝛾𝑢1,𝑟
1,1
(𝑢1, 𝑢2) of user 𝑢1, as written below
𝛾𝑢1,𝑟
1,1
(𝑢1, 𝑢2) =
𝑃1,1,𝑢1,𝑟
∑ ∑ 𝑃𝑖,𝑗,𝑢1,𝑟
3
𝑗=1
𝑁s
𝑖=1 − 𝑃1,1,𝑢1,𝑟
, (3)
Therefore, the objective is to maximize the achievable throughput per group 𝑅 𝑇,𝑢,𝑔 for each cell and site. For
the case of cell 𝑐 and site 𝑠, the throughput per group 𝑅 𝑇,𝑢,𝑔 to be maximized is
𝑅 𝑇,𝑢,𝑔 = ∑ 𝛾𝑢 𝑛,𝑟
𝑐,𝑠
(𝑢1, ⋯ , 𝑢 𝑁 𝑢𝑔
)
𝑁 𝑢𝑔
𝑛=1
, (4)
where the number of users per group is 𝑁𝑢𝑔. In the next section, the nature-inspired heuristic models for
NOMA systems will be studied and compared against the proposed ACO scheme.
4. NATURE-INSPIRED HEURISTIC MODELS FOR NOMA SYSTEMS
Most of technologies which are created by human beings are inspired from nature such as insects’
behaviors [43]. This paper reviews this nature inspired heuristic models which are the candidates to be
considered in finding a solution for getting the best user pairs in a wireless network.
4.1. Drosophila optimization model
Similar to ant-colony optimization, this method is inspired by the behaviour of drosophila of searching
for food [35]. The concept of foraging behaviour is used for optimization. All drosophila fly to a particular location

once the location of fruit fly is found. Drosophila is able to detect the food location as it has a strong sense of
smell and wide vision. It has the capability to look for food source within 25 miles. This optimization works by
initializing the position of drosophila’s group based on range of variation for each individual. It follows by
providing information on the direction and distance which are dependent on the characteristics of fruit fly when
foraging. The flavour concentration determination value is then introduced.
The function of the density and the determination values are used to obtain the best flavour for
drosophila’s group. At the same time, the optimum solution is discovered. Drosophila optimization is easy to be
implemented when dealing with practical problems. It can be used to solve positive real problems with high
precision. It has been demonstrated in [35] that Drosophila Optimization is useful for the large antenna systems,
which are proposed to be one of the key features in 5G along with NOMA. However, when handling with non-
positive (complex) problem, its stability seizes. Hence, users are not encouraged to apply this algorithm when
dealing with negative arguments which cannot be interpreted or processed [36].
4.2. Particle swarm optimization model
Particle Swarm Optimization (PSO) is proposed in year 1995 by James Kennedy and Russell C.
Eberhert. The principle of this method is wholly–based on the ability of groups. It is inspired by the concept of
looking for food and process of courtship. The word ‘particle’ in PSO refers to bird [36]. The particle/bird switches
its speed and direction based on information to maintain the optimum state and reach the right position.
The solution is obtained only after a certain number of continuous iterations. For each cycle or iteration, two
extremes are observed. The first extreme is known as ‘personal best’ which is identified by particles (bird) as
optimal solution. The entire population observes the second extreme, the ‘global maximum’ as optimal solution.
These two parameters are important in determining the best solution/answer and speed in particular
direction. PSO comprises of simple principles and less variables which make it is easy to be implemented.
Moreover, it can be used in dealing with a wide range of issues such as non-linear, non-differentiable and
so on [44]. In [45], PSO has been proposed to reduce the access delay in 5G systems. In addition to this, PSO has
been demonstrated to improve power allocation in NOMA, as presented in [46]. Although it reduces the
computational complexity required when non-heuristics models are used, no comparison has been made with
other heuristic models such as ACO for NOMA systems. The number of iterations used in the proposed PSO
model is relatively high, which goes up to 200 and the number of considered users are only between 2 to 4 users
per sub-channel. Furthermore, PSO does not work for some problems like non-coordinate system.
4.3. Firefly optimization model
Likewise the heuristic algorithms mentioned above, this optimization tool is inspired by the behaviour
of fireflies [36]. The flashing behaviour of fireflies is the underlying principle of this method. It consists of two
parameters which evaluate and indicate the accuracy of the solution. The two indicators are fluorescence value
and radius of perception. The fluorescence value is used to determine the quality of position for individual. On
the other hand, the individual search is measured by the size of radius of perception. Initially, the individual
(firefly) moves to the location of outstanding individual which is located within its area of search.
The attractiveness of the brightness of firefly (due to fluorescence intensity) is essential to indicate the target.
The location of target can be easily detected with better brightness. The attractiveness will be higher as
the brightness is obvious. Thus, the target is clearly illustrated.
Factors such as fluorescence intensity and relative attractiveness must be addressed well when applying
this technique to ensure the performance of the algorithm. The distance is inversely proportional to both
the brightness and attractiveness. Firefly Optimization is easy to operate and it makes use of a small size of
parameters. The negative point of this method takes a long time for converging and has a low probability of peak
detection [36]. Firefly has also been employed in telecommunications, as studied in [39] for instance. Although
firefly algorithm has been implemented for clustering sensor nodes or users in wireless sensor networks [47],
the user grouping issue in NOMA has not been addressed.
4.4. Dolphin echolocation model
This approach is said to be similar to the rest of the optimization tools in as it shares some common
characteristics. Therefore, the dolphin echolocation (DE) can be applied to find solutions for optimization
issues. The concept of DE is based on the behaviour of a dolphin when foraging for prey. Initially, the dolphin
looks for food within its search region and then focuses on a particular location once the target (prey) is found.
It reduces the range of its search area as it approaches the prey. In other words, the echolocation process is
proportional to the distance from the target. DE consists of two phases in its algorithm. Firstly, the dolphin
initiates a global search where it searches the unexplored areas by choosing random paths within
the search/look-for region and obtains some initial observation. In the second phase, the search range is limited
or restricted to a smaller area and focuses on a particular location in a specific direction. This reduction of

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look-for region is based on the results provided by the first phase. Most of the optimization tools apply these
two phases in determining the optimal solution for a problem. The dolphin echolocation has the ability to adapt
its algorithm according to the issues in which the technique is implemented. Hence, it can be used for solving
various problems. In contrast, there is a high probability of information leakage occurrence in DE. Insufficient
information affects the accuracy and stability of solution. This is due to the absence of essential data in
determining and computing the optimal solution. Besides that, echolocation is only applicable within a limited
range. Thus, it has to be implemented multiple times in different locations in order to have a wider range of
search space [40, 48, 49]. Therefore, implementing DE in a user scheduling problem with a significantly wider
search space such as NOMA with a large number of antennas is prohibitive.
4.5. Ant-colony optimization model
Ant-Colony optimization is an optimization tool which can be used for finding optimal or shortest
paths based on the ants’ behaviour in searching for their food. An ant generally moves in a random fashion
prior finding the food. When the food is found, the ant will return to its colony leaving markers, known as
pheromones, that instigate other ants to take the same path for finding the food [19]. The concentration of
pheromone in a particular (optimal) path depends on the number of ants using the same particular path to reach
the food location. Thus, the evaporation rate of the pheromone will be low which in turn attracts more ants to
use the path. In other words, ACO is a population-based meta-heuristic method that can be used to find an
approximate solution to difficult optimization problems. The artificial ants look for a good solution to a given
optimization problem. ACO is a strong technique which can be integrated with other heuristic algorithms to
improve the performance of its own and other algorithms. Besides that, ACO has the ability to find a better
solution in solving the problem. The proposed ACO algorithm [50] for NOMA is as follows
Input : The SINR of each user before sharing the bandwidth, the number of ants, 𝑁𝑎𝑛𝑡𝑠, the number 𝑁𝑢
of users, the number 𝑁𝑢𝑒 of users per group, the initial pheromone value, the pheromone value,
the probability of choosing a user.
Output : The users in each group and the SINR of each user after sharing the bandwidth (NOMA)
Steps :
(i) The number of nodes of the ant colony optimization is set to 𝑁𝑎𝑛𝑡𝑠 and the number of stages is also
configured to𝑁𝑎𝑛𝑡𝑠.
(ii) Every ant will choose the next node, which is not yet chosen previously based on
the initialized heuristic information, the pheromone value and its evaporation rate and
the probability as described in [50].
(iii) The path with the maximum throughput per group, 𝑅 𝑇,𝑐,𝑠 as given in (4), which serves as the objective
function, will be updated as well as the pheromone value, the evaporation rate and the probability before
the next iteration is run.
(iv) The algorithm is terminated when the convergence criterion is fulfilled or the maximum number of
iterations is reached.
Table 1 summarizes the pros and cons for all heuristics algorithms discussed in this paper. The best
technique can be chosen based on the requirements set by the users. Since each of the heuristic models has plus
points in different aspects, the best algorithm will be the one that suits the research scope and problems
addressed. For grouping and scheduling the users in NOMA, the best heuristic algorithm presented in literature
so far is ACO. It has been demonstrated to be able to group the users in cellular networks [19]. Other heuristic
algorithms are suitable for other applications. PSO algorithms have been applied to improve power allocation
in NOMA, as presented in [44-46]. Apart from improving the power allocation, PSO is also suitable for
reducing the access delays in NOMA. However, the current general version of PSO is not suitable for grouping
the users in NOMA.
Table 1. Shows the advantages and disadvantages of each heuristic algorithm
Nature-Inspired Algorithms Advantage Disadvantage
Ant-Colony Optimization [19]  Easy to be implemented
 Ability to integrate with another
heuristic algorithm
 Requires longer search time
Drosophila Optimization [35]  Deals with practical problems
 High precision
 Unable to use for non-positive issue
Particle Swarm Intelligence [36]  Easy to implement
 Consists of less variables
 Limitation on application of algorithm
Firefly Optimization [39]  Easy to operate
 Less parameters involved
 Low convergence rate
 Low peak detection
Dolphin Echolocation [40]  Adaptation to various problems  Information leakage
 Limited range

5. RESULTS
A NOMA system is considered to provide radio resources for users by grouping these users in pairs
with one resource block allocated per pair or group. The number of available resource blocks is varied
from 1, 2, 3, 4 to 5 resource blocks with the number of available users varied as 2, 4, 6, 8 and 10
accordingly. In order to test the proposed ACO user grouping scheme, two other schemes are also executed
which are the proportional fairness fixed allocation (PFFP) [27] and the exhaustive search (ES) method that
serves as the upper bound since it finds the global optimum grouping. Figure 1 shows the mean throughput
obtained when these approaches implemented in the NOMA system.
Figure 1. The mean throughput in Mbps vs the number of resource blocks
On the whole, the mean throughputs achieved by all three schemes under consideration increase as
the number of resource blocks increases. When the number of resource blocks is between one and two,
the mean throughput increases slowly and gradually. A more significant increase in the mean throughput can
be observed when the number of resource blocks is between two and four. The gradient of the graph is
the steepest when the number of resources falls in the range of four to five. It can be seen from this figure that
the achievable mean throughput by ACO is higher than that of the PFFP. The gap between these two mean
throughputs grows larger as the number of resource block increases. The mean throughput recorded for ACO
is very close to the upper bound which is obtained via the exhaustive search (ES) method, where all possible
combinations of user pairings are tested to find the maximum mean throughput. On the other hand, the PFFP
method which is proposed in [27] to reduce the computational time and load, is observed to produce the lowest
performance in terms of the achievable mean throughput. As a conclusion, the proposed ACO scheme has been
demonstrated to provide the best user-pairs which maximizes the mean throughput, hence the spectral
efficiency, close to the upper bound.
6. FUTURE WORK
This paper reviews some heuristic models which are potential to be regarded as candidate algorithms
in NOMA for determining the best user groups. The main objectives of using this heuristic model are to reduce
the computational load and increase the throughput of NOMA systems. Based on our concise review, ACO is
a potential scheme to be considered for NOMA user scheduling as it has been demonstrated to be successfully
employed for user grouping in cellular networks for the given radio bandwidth. Dolphin echolocation is not
suitable as it is only applicable over a limited range of input space. However, ACO has no limitation on how
many variables it has to work with. Firefly optimization and particle swarm optimization require a smaller
number of parameters but so far implemented with a small number of users per sub channel. The practical
solution provided by ACO [19, 42, 51] method for NOMA is useful as the number of mobile users using
the cellular network services keeps increasing in this age. It is a method worth to be implemented and further
developed and presented in the research and academic community. Therefore, a further study on ACO
implementation with NOMA is the way forward to improve performance of user grouping along with other
potentially effective improvements in the future.

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7. CONCLUSION
Computational complexity in computing the best user pairs or groups becomes a barrier or challenge
to achieve high throughput in NOMA. Therefore, ant-colony optimization (ACO) is considered to be applied
in NOMA for solving the optimization problem. The increasing number of users, which will increase
the required computational load without a good solution such as our proposed ACO-based approach, is an
inevitable trend in this current age. As this ACO-based user grouping scheme has been proposed to perform
well for the users sharing the same bandwidth in SC-FDMA and OFDMA networks, this scheme is potentially
viable to be further developed for the future systems such as NOMA in 5G networks and beyond. Although
the discussion and result in this paper is limited to two users per group for NOMA systems, further and future
research can be carried out when the number of users is more than two per group. Therefore, employing ACO
to perform user grouping for NOMA will certainly be useful for 5G networks and beyond.
ACKNOWLEDGEMENTS
The authors acknowledge and thank all the support from the Ministry of Education Malaysia for
funding this research under the FRGS fund, with the grant code of FRGS/1/2019/TK04/MMU/02/2. Not to
forget the support from Faculty of Engineering and Technology, Multimedia University, as well as all other
individuals who are directly or indirectly involved in preparing this paper.
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