Enhancing 5G network performance through effective resource management with network slicing

Abstract

The immense growth of mobile networks leads to versatile applications and new demands. The improved concert, transferability, flexibility, and performance of innovative network services are applied in diversified fields. More unique networking concepts are incorporated into state-of-the-art mobile technologies to expand these dynamic features further. This paper presents a novel system architecture of slicing and pairing networks with intra-layer and inter-layer functionalities in 5th generation (5G) mobile networks. The radio access network layer slices and the core network layer slices are paired up using the network slicing pairing functionalities. The physical network elements of such network slices will be logically assigned entities called softwarization of the network. Such a novel system architecture called network sliced softwarization of 5G mobile networks (NSS-5G) has shown better performances in terms of end-to-end delay, total throughput, and resource utilization when compared to traditional mobile networks. Thus, effective resource management is achieved using NSS-5G. This study will pave the way for future softwarization of heterogeneous mobile applications. This is an open access article under the CC BY-SA license.

Full text

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 14, No. 4, August 2024, pp. 4721~4731
ISSN: 2088-8708, DOI: 10.11591/ijece.v14i4.pp4721-4731  4721
Journal homepage: http://ijece.iaescore.com
Enhancing 5G network performance through effective resource
management with network slicing
Nagarajan Suganthi1, Enthrakandi Narasimhan Ganesh2, Elangovan Guruva Reddy3,
Vijayaraman Balakumar4, Thangam Ilakkiya5, Mageshkumar Naarayanasamy Varadarajan6,
Venkatachalam Ramesh Babu7
1Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, India
2Department of Electronics and Communication Engineering, St. Peters Institute of Higher Education and Research (SPIHER),
Chennai, India
3Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India
4Sub Divisional Engineer (Security), MPLS Network Operations Centre, Bharat Sanchar Nigam Limited, Bengaluru, India
5Department of Management Studies, Sri Sai Ram Institute of Technology, Chennai, India
6Lead Software Engineer, Capital One, Glen Allen, United States
7Department of Computer Science and Engineering, Dr.MGR Educational and Research Institute, Chennai, India
Article Info
ABSTRACT
Article history:
Received Oct 2, 2023
Revised Mar 26, 2024
Accepted May 12, 2024
The immense growth of mobile networks leads to versatile applications and
new demands. The improved concert, transferability, flexibility, and
performance of innovative network services are applied in diversified fields.
More unique networking concepts are incorporated into state-of-the-art
mobile technologies to expand these dynamic features further. This paper
presents a novel system architecture of slicing and pairing networks with
intra-layer and inter-layer functionalities in 5th generation (5G) mobile
networks. The radio access network layer slices and the core network layer
slices are paired up using the network slicing pairing functionalities. The
physical network elements of such network slices will be logically assigned
entities called softwarization of the network. Such a novel system
architecture called network sliced softwarization of 5G mobile networks
(NSS-5G) has shown better performances in terms of end-to-end delay, total
throughput, and resource utilization when compared to traditional mobile
networks. Thus, effective resource management is achieved using NSS-5G.
This study will pave the way for future softwarization of heterogeneous
mobile applications.
Keywords:
5G Mobile
Network slicing
Pairing networks
Resource management
Softwarization
Virtualization
This is an open access article under the CC BY-SA license.
Corresponding Author:
Nagarajan Suganthi
Department of Computer Science and Engineering, SRM Institute of Science and Technology
Ramapuram Campus, Chennai, Tamil Nadu, India
Email: suganthn@srmist.edu.in
1. INTRODUCTION
Mobile networks are in dynamic progress over some time to gratify the new-fangled demands for
improved performance, transportability, springiness, and resource management of innovative network
services. 5th generation (5G) mobile networks espouse state-of-the-art networking architecture to meet the
ongoing needs of modern life [1]. 5G mobile networks afford enormous scheme functionalities with high-
speed data transfer, less end-to-end delay, enhanced dependability, and state-of-the-art experience in internet
of things (IoT) applications [2]. The telecommunication regulatory authorities and the research fraternity are
putting vast exertions into developing a novel model called the softwarization of 5G mobile networks.
Multiple latest concepts, like software networks, network function virtualization, are integrated to meet

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newer service requirements. The stage-wise network transformation for the softwarization of mobile
networks is presented in Figure 1.
.
Figure 1. Softwarization of 5G mobile networks
The establishment of 5G mobile networks is explained in four stages towards achieving
softwarization [3]. In stage 1, recitals, including software-defined networks and network function
virtualization, are designed, leading to network virtualization and clouding. In stage 2, migration of services,
including wide area network establishment of software-defined networks and cloud management, is
established. In stage 3, the transposition of the network and services, including end-to-end orchestration of
network elements, are furnished. In stage 4, automation of service networks, including network slicing and
edge computing of multi-access technologies, are developed. In the architecture, the obstinate rigid mobile
networks are visualized as dynamic and elegant software-based applications [4]. These design vicissitudes
are beneficial to real-time industry applications and end-user requirements. This will also lead to the
engenderment of novel service models and innovative value chains that impact cultural and social conduct.
Based on the functionalities and the operational convenience, the system architecture of 5G mobile
networks is separated into multiple functional layers: the business process, control, and infrastructure. The
concept of network softwarization empowered the capability to characterize the 5G mobile network as an
encrusted structure designed analogous to software-defined networks [5]. The system architecture of 5G
mobile networks supports various heterogeneous applications of mobile devices and various internet of
things (IoT) applications. The network-connected elements, including base stations, switches, hubs, and
routers, are connected to the infrastructure layer. In contrast, all the network control modules and decision
support units are positioned in the control layer. Application service requests are initiated at the business
process layer, which is communicated to the control layer and translates into service control instructions.
Each layer associates and communicates with the adjacent layer, and at the same time, all the layers are

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operationally synchronized with the end-to-end management and establishment interface [6]. Optical
transport networks and other backhaul access technologies function in the infrastructure layer. Various
configuration setup procedures and network operating systems are performed in the control layer. Functional
service-level applications, enterprise, and other third-party applications function in the business process
layer. An anti-phishing system was proposed to immediately remove features from the uniform resource
locator and utilize four classification techniques: decision tree, K-nearest neighbor, support vector machine,
and random forest [7]. The system is necessarily a global positioning system and a global system for mobile
telecommunications technology. The vehicle's position on the ground is decided utilizing Google Maps [8].
Mobile phones and smartphone software that exhibit sensor data and alarms of falls have also been
introduced. It admits a web interface. To raise the sensor node service life, the pressure in the center element
essentially utilizes a full-strength, flexible solar-oriented sensor [9]. To increase the central ingredient life of
the wearable sensor, the pressure in the center ingredient mostly utilizes a full-strength flexible solar-oriented
sensor with the effect of a superior method similar to maximum power point tracking [10].
A slicing network can improve efficiency and offer various 5G services through a virtual network.
Network slicing can separate the 5G physical network [11]. Radio resource management in 5G new radio
featuring network slicing during a mixed integer linear program [12]. Network slicing represents an
important concept proposed in 5G networks to maintain the requirements articulated by a service generation.
network slicing creates chances for service providers and virtual network operators, appropriating them to
run their virtual, self-governing functions on a shared structure [13]. An efficient packet-based scheduling
mechanism for data traffic via 5G slicing with two function modes enhances the 5G cloud resource utilization
and offers a competent separation of the 5G slices [14]. The dynamic resource allocation method improves
resource allocation, enhances service capacity, and meets the necessary service quality of the several tenants
[15]. An optimal resource allocation is demonstrated using an adaptive channel bandwidth option to calculate
service quality requirements and traffic aggregation priority. It improved the efficiency of licensed radio
resources by improving the long term evolution (LTE) frame formation process and minimizing signal traffic
distribution [16].
2. METHOD
Implementing the proposed architecture provides manifold connectivity and acquaintances in
disseminated and consolidated network platforms. Such networking mechanisms are used to accomplish
distinct degrees of access management and enrich interface communication between base stations. Network
capabilities of the imminent 5G mobile networks will be ascending to meet the versatile requirements in a
corporate environment. So, the functionalities of access and core networks need to be further boosted to meet
the growing demands. The system architecture of the 5G mobile networks has adapted for effective resource
management in terms of end-to-end delay, total throughput, and resource utilization using network slicing
and softwarization.
2.1. Network slicing in 5G networks
Network slicing in 5G mobile networks is established by integrating virtualization and
softwarization of self-governing logical networks on the identical physical network substructure [17]. An
individual network slice is a secluded end-to-end network personalized to accomplish varied necessities
entreated by a specific application. Multiple service level agreements can be met with the pivotal role
functionalities of network slicing in softwarization of 5G mobile networks. This leads to deploying supple
and ascendable network slices over the shared network architecture [18]. From an industrial application
model viewpoint, each network slice is controlled by a virtual network operator. The telecom service
provider tenancies its logical functionalities to the virtual operator, segmenting the fundamental physical
infrastructure [19]. Based on the accessibility of the allotted resources, the service provider can
independently organize various adapted network slices to meet the requirements of specific applications.
Softwarization in 5G networks to sustenance functionalities such as improved mobile coverage and
dependable low-latency data transfer has transfigured the industry standards [20]. The pseudo-code for
network slicing in 5G mobile networks is explained in Table 1.
Implementing this algorithm and the defined mathematical expressions will allow us to effectively
manage resources using network slicing in 5G networks. The algorithm dynamically allocates resources
based on network slice requirements, resulting in improved network performance, reduced delay, increased
throughput, and optimized resource utilization. Additionally, integrating machine learning algorithms enables
proactive resource allocation adjustments based on predicted traffic patterns, enhancing the network's
performance.

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Table 1. Pseudo code for network slicing in 5G mobile networks
Sl. No
Pseudocode
1
Define network slicing parameters:
𝐵𝑎𝑛𝑑𝑤𝑖𝑑𝑡ℎ (𝐵)
𝐿𝑎𝑡𝑒𝑛𝑐𝑦 (𝐿)
𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 (𝐴)
2
Collect network data:
Traffic patterns
Resource usage
3
Determine network slice requirements:
The bandwidth requirement for a specific network slice (B_slice)
Latency requirement for a particular network slice (L_slice)
Availability requirement for a particular network slice (A_slice)
4
Slice the network based on requirements:
Allocate bandwidth for the network slice:
𝐵𝑎𝑛𝑑𝑤𝑖𝑑𝑡ℎ 𝐴𝑙𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛 = (𝐵 × 𝐵_𝑠𝑙𝑖𝑐𝑒)/(𝑇𝑜𝑡𝑎𝑙 𝑁𝑒𝑡𝑤𝑜𝑟𝑘 𝑆𝑙𝑖𝑐𝑒 𝐵𝑎𝑛𝑑𝑤𝑖𝑑𝑡ℎ 𝑅𝑒𝑞𝑢𝑖𝑟𝑒𝑚𝑒𝑛𝑡𝑠)
Allocate latency for the network slice:
𝐿𝑎𝑡𝑒𝑛𝑐𝑦 𝐴𝑙𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛 = 𝑚𝑖𝑛(𝐿, 𝐿_𝑠𝑙𝑖𝑐𝑒)
Allocate availability for the network slice:
𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝐴𝑙𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛 = 𝐴_𝑠𝑙𝑖𝑐𝑒
5
Monitor network performance and adjust resource allocation as necessary:
Calculate end-to-end delay:
𝐸𝑛𝑑 − 𝑡𝑜 − 𝐸𝑛𝑑 𝐷𝑒𝑙𝑎𝑦 = 𝑡_𝑟𝑒𝑐𝑒𝑖𝑣𝑒 − 𝑡_𝑠𝑒𝑛𝑑
Calculate total throughput:
𝑇𝑜𝑡𝑎𝑙 𝑡ℎ𝑟𝑜𝑢𝑔ℎ𝑝𝑢𝑡 = (𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑏𝑖𝑡𝑠 𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑡𝑡𝑒𝑑)/(𝑇𝑜𝑡𝑎𝑙 𝑡𝑖𝑚𝑒 𝑡𝑎𝑘𝑒𝑛)
Calculate resource utilization:
𝑅𝑒𝑠𝑜𝑢𝑟𝑐𝑒 𝑈𝑡𝑖𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛 = (𝑇𝑜𝑡𝑎𝑙 𝑢𝑠𝑒𝑑 𝑏𝑎𝑛𝑑𝑤𝑖𝑑𝑡ℎ) / (𝑇𝑜𝑡𝑎𝑙 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑏𝑎𝑛𝑑𝑤𝑖𝑑𝑡ℎ)
6
Use machine learning algorithms to predict future traffic patterns and adjust resource allocation preemptively:
𝑇𝑟𝑎𝑓𝑓𝑖𝑐 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛 = 𝑓(𝑇𝑟𝑎𝑓𝑓𝑖𝑐 𝐷𝑎𝑡𝑎)
Resource Allocation Adjustment:
If Traffic Prediction indicates an increase in traffic, allocate additional resources to meet the predicted demand
7
Mathematical Expressions:
Bandwidth Allocation:
𝐵𝑎𝑛𝑑𝑤𝑖𝑑𝑡ℎ 𝐴𝑙𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛 = (𝐵 𝑥 𝐵_𝑠𝑙𝑖𝑐𝑒)/(𝑇𝑜𝑡𝑎𝑙 𝑁𝑒𝑡𝑤𝑜𝑟𝑘 𝑆𝑙𝑖𝑐𝑒 𝐵𝑎𝑛𝑑𝑤𝑖𝑑𝑡ℎ 𝑅𝑒𝑞𝑢𝑖𝑟𝑒𝑚𝑒𝑛𝑡𝑠)
Latency Allocation:
𝐿𝑎𝑡𝑒𝑛𝑐𝑦 𝐴𝑙𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛 = 𝑚𝑖𝑛(𝐿, 𝐿_𝑠𝑙𝑖𝑐𝑒)
Availability Allocation:
𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝐴𝑙𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛 = 𝐴_𝑠𝑙𝑖𝑐𝑒
End-to-End Delay:
𝐸𝑛𝑑 − 𝑡𝑜 − 𝐸𝑛𝑑 𝐷𝑒𝑙𝑎𝑦 = 𝑡_𝑟𝑒𝑐𝑒𝑖𝑣𝑒 − 𝑡_𝑠𝑒𝑛𝑑
Total Throughput:
𝑇𝑜𝑡𝑎𝑙 𝑡ℎ𝑟𝑜𝑢𝑔ℎ𝑝𝑢𝑡 = (𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑏𝑖𝑡𝑠 𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑡𝑡𝑒𝑑)/(𝑇𝑜𝑡𝑎𝑙 𝑡𝑖𝑚𝑒 𝑡𝑎𝑘𝑒𝑛)
Resource Utilization:
𝑅𝑒𝑠𝑜𝑢𝑟𝑐𝑒 𝑈𝑡𝑖𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛 = (𝑇𝑜𝑡𝑎𝑙 𝑢𝑠𝑒𝑑 𝑏𝑎𝑛𝑑𝑤𝑖𝑑𝑡ℎ)/(𝑇𝑜𝑡𝑎𝑙 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑏𝑎𝑛𝑑𝑤𝑖𝑑𝑡ℎ)
2.2. Effective resource management using network slicing
There have been several studies on managing resources effectively in future mobile technologies.
This includes increasing the network capacity to enhance the coverage quality, using logical functionalities
instead of the physical elements, tractability, and general progress practices in real-time applications [21]. To
achieve effective resource management, two factors are involved in the technological modernization and
collective advancement of mobile networks [22], [23]. They are establishing virtualized network architecture
and developing robust transportation infrastructure. When comparing the proposed 5G mobile networks
concerning the traditional supported hardware transportation infrastructure, there needs to be improvisation
in virtualization and softwarization of physical resources [24]. There, the high cost involved in resource
allocation can be minimized. This can be achieved by partitioning the control, infrastructure, and business
process layers, as shown in Figure 2.
In the softwarization of 5G mobile networks, logical components are devoted to dynamic capacity
building to meet heterogeneous requirements [25]. Using the same set of processors and servers, the concept
of network slicing certificates the conception of slices ardent to logical, autonomous, and apportioned
network functionalities. In Figure 3, the pairing of network slicing elements is established [26].
Communication can be intra-layer or through inter-layer slices. In the radio access network, multiple
slices can communicate with each other, and in unison, each radio access network slice can be paired with
each slice on the core network layer [27]. Such resource management of pairing of network slices can be
effective in the performance of mobile networks. The pseudo-code representation of effective resource
management using network slicing is shown in Table 2, and the block diagram representation of the proposed
system architecture is shown in Figure 4.

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Figure 2. System architecture of 5G mobile networks
Figure 3. Pairing of network slices

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Table 2. Pseudo code for effective resource management
Sl. No
Pseudocode
1
Define the network slicing parameters such as bandwidth, latency, and availability.
Bandwidth = B //Total available bandwidth Latency = L //Maximum tolerable end-to-end latency Availability = A
//Minimum required availability percentage
2
Collect network data, including traffic patterns and resource usage
3
Determine the network slice requirements based on the data collected.
Network Slice Bandwidth Requirement = B_slice //Bandwidth requirement for a particular network slice Latency
Requirement = L_slice //Latency requirement for a special network slice Availability Requirement = A_slice //Availability
requirement for a specific network slice
4
Slice the network based on the requirements and allocate resources accordingly.
Bandwidth Allocation = (B x B_slice) / (Total Network Slice Bandwidth Requirements) Latency Allocation = min(L,
L_slice) Availability Allocation = A_slice
5
Monitor network performance and adjust resource allocation as necessary.
Network Performance Metrics: End-to-End Delay = t_receive - t_send //time taken for a packet to reach th the receiver
from the sender Throughput = (Number of bits transmitted) / (Total time taken) Resource Utilization = (Total used
bandwidth) / (Total available bandwidth)
6
Use machine learning algorithms to predict future traffic patterns and adjust resource allocation preemptively.
Machine Learning Algorithm: Traffic Prediction = f(Traffic Data) //Predict future traffic patterns.
Resource Allocation Adjustment: If Traffic Prediction indicates an increase in traffic, allocate additional resources to the
network slices to meet the predicted demand
The physical properties of the network elements are converted as logical functionalities using
virtualization and softwarization in 5G mobile networks. Then, using intra-layer and inter-layer slicing, the
pairing of slices is incorporated for effective resource management. Then, the proposed system architecture is
simulated in a laboratory environment to compare performance metrics regarding the end-to-end delay, total
throughput, and resource utilization. The simulated metrics are compared with the traditional mobile
networks, which are taken as benchmarks.
Figure 4. Block diagram representation of proposed system
3. SIMULATION ANALYSIS
The viability and the effectiveness of the proposed resource management architecture using network
slicing and softwarization are evaluated in the network simulator-3 simulation platform. The simulations are
accomplished at a system-level model to establish proof of concept [28], [29]. The benchmark values for the
end-to-end delay, total throughput, and resource utilization percentage are taken from traditional 4G mobile
networks without network slicing and softwarization. The network's traffic network is taken as a reference
for the simulation, and the corresponding performance metrics are measured. The simulation environment is
shown in Table 3.

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Table 3. Simulation results
Network simulator
NS-3 (version 3.32)
Simulation duration
100 seconds
Traffic model
Poisson
Network topology
This realistic 5G network topology has three base stations, ten user equipment, and one core
network element.
Mobility model
Random Waypoint
Network slicing Configuration
Proposed network sliced softwarization of 5G mobile networks (NSS-5G) algorithm and
compared it with a traditional Benchmark
The definitions of network delay, throughput, and resource utilization are below. The delay metric
measures the end-to-end delay experienced by network traffic. It quantifies the time it takes for a packet to
travel from the sender to the receiver. Lower delay indicates faster communication and better user
experience. The total Throughput metric measures the data transmitted over the network during the
simulation. It represents the network to handle data traffic. Higher throughput indicates better network
performance and higher data transmission rates. Resource Utilization metric evaluates the efficiency of
resource allocation in the network and measures the percentage of available resources (e.g., bandwidth)
utilized by the network slices. Higher resource utilization indicates more efficient resource allocation and
better network efficiency. The analysis shows that NSS-5G networks have minimum end-to-end delay
compared to conventional 4G networks, as shown in Figure 5.
Figure 5. Delay performance analysis
The following implications are from the simulation analysis for effective resource management of
5G mobile networks using network slicing and softwarization. The delay performance analysis for the NSS-
5G is compared with the traditional 4G mobile networks, a benchmark. As shown in Figure 6, the total
throughput performance analysis for the NSS-5G is compared with the traditional 4G mobile networks. The
study revealed that NSS-5G networks have higher throughput efficiency than conventional 4G networks.
The following implications are from the simulation analysis for effective resource management of
5G mobile networks using network slicing and softwarization. The delay performance analysis for the NSS-
5G is compared with the traditional 4G mobile networks, a benchmark. As shown in Figure 6, The total
throughput performance analysis for the Netwonet worked and NSS-5G is compared with the traditional 4G
mobile networks. The study revealed that NSS-5G networks have higher throughput efficiency than
conventional 4G networks. The resource utilization performance of the NSS-5G is compared with the
benchmark mobile networks, as shown in Figure 7. The analysis shows that the resource utilization
performance is better for the proposed system, like NSS-5G, than traditional benchmarks. From the
simulation results, the NSS-5G mechanism reached 80 resource utilization compared to the traditional
benchmark mechanism.

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Figure 6. Delay performance analysis
Figure 7. Resource utilization performance
4. CONCLUSION
The current work proposes an innovative model network using network slicing and softwarization of
5G Mobile networks. The system architecture, including intra-layer and inter-layer communication between
the radio access and core network layers, leads to effective resource utilization in the proposed NSS-5G.
Furthermore, the proposed architecture uses logical establishment to empower suppleness and multiple
resource management features. Based on the slice requirements and individual pairing of network slices and
the notarization of the network elements to handle data traffic communication, the proposed system proves to
have better performance in terms of end-to-end delay, total throughput, and resource utilization. With the
increasing demand for heterogeneous applications involving multiple technologies, effective management of
resources in 5G mobile networks leads to dynamic application features in the business environment. This
simulation analysis and results afford groundwork for future research on network slicing and softwarization
of next-generation mobile networks. The heterogeneous model involving multiple access technologies can be
explored with diversified network resource administration architecture as a next step.
REFERENCES
[1] R. Khan, P. Kumar, D. N. K. Jayakody, and M. Liyanage, “A survey on security and privacy of 5G technologies: potential
solutions, recent advancements, and future directions,” IEEE Communications Surveys & Tutorials, vol. 22, no. 1, pp. 196–248,
2020, doi: 10.1109/comst.2019.2933899.
[2] M. J. Shehab, I. Kassem, A. A. Kutty, M. Kucukvar, N. Onat, and T. Khattab, “5G networks towards smart and sustainable cities:
a review of recent developments, applications and future perspectives,” IEEE Access, vol. 10, pp. 2987–3006, 2022, doi:
10.1109/access.2021.3139436.
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80
100
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Resource Utlization(%)
Traffic Load
Resource Utlization
NSS-5G Traditional Benchmark

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[3] R. Raman, S. Muthumarilakshmi, G. Jethava, R. Jagtap, M. Lalitha, and S. Murugan, “Energy monitoring in solar-powered
buildings using internet of things,” in 2023 2nd International Conference on Smart Technologies for Smart Nation,
SmartTechCon 2023, Aug. 2023, pp. 318–322, doi: 10.1109/SmartTechCon57526.2023.10391826.
[4] J. Cao et al., “A survey on security aspects for 3GPP 5G networks,” IEEE Communications Surveys & Tutorials, vol. 22, no. 1,
pp. 170–195, 2020, doi: 10.1109/comst.2019.2951818.
[5] Z. H. Hussien and Y. Sadi, “Flexible radio resource allocation for machine type communications in 5G cellular networks,” in
2018 26th Signal Processing and Communications Applications Conference (SIU), May 2018, pp. 1–4, doi:
10.1109/SIU.2018.8404840.
[6] C.-C. Teng, M.-C. Chen, M.-H. Hung, and H.-J. Chen, “End-to-end service assurance in 5G crosshaul networks,” in 2020 21st
Asia-Pacific Network Operations and Management Symposium (APNOMS), Sep. 2020, pp. 306–309, doi:
10.23919/APNOMS50412.2020.9236977.
[7] A. Nandi Tultul, R. Afroz, and M. A. Hossain, “Comparison of the efficiency of machine learning algorithms for phishing
detection from uniform resource locator,” Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), vol. 28,
no. 3, pp. 1640–1648, Dec. 2022, doi: 10.11591/ijeecs.v28.i3.pp1640-1648.
[8] N. I. Akanda, M. A. Hossain, M. M. I. Fahad, M. N. Rahman, and K. Khairunnaher, “Cost-effective and user-friendly vehicle
tracking system using GPS and GSM technology based on IoT,” Indonesian Journal of Electrical Engineering and Computer
Science (IJEECS), vol. 28, no. 3, pp. 1826–1833, Dec. 2022, doi: 10.11591/ijeecs.v28.i3.pp1826-1833.
[9] S. Yuvarani, A. Gayathri, K. J. Velmurugan, V. Meenakshi, S. Sadhana, and C. Srinivasan, “Quality of service factor based
unfailing route formation in wireless sensor network,” in 2023 International Conference on Intelligent and Innovative
Technologies in Computing, Electrical and Electronics (IITCEE), Jan. 2023, pp. 617–622, doi:
10.1109/IITCEE57236.2023.10090966.
[10] A. P, M. Meenakumari, S. L, R. N, S. Jayaprakash, and S. Murugan, “Intelligent power control models for the IOT wearable
devices in BAN networks,” in 2023 International Conference on Intelligent and Innovative Technologies in Computing, Electrical
and Electronics (IITCEE), Jan. 2023, pp. 820–824, doi: 10.1109/IITCEE57236.2023.10090918.
[11] S. A. AlQahtani, “Cooperative-aware radio resource allocation scheme for 5G network slicing in cloud radio access networks,”
Sensors, vol. 23, no. 11, May 2023, doi: 10.3390/s23115111.
[12] K. Boutiba, M. Bagaa, and A. Ksentini, “Optimal radio resource management in 5G NR featuring network slicing,” Computer
Networks, vol. 234, Oct. 2023, doi: 10.1016/j.comnet.2023.109937.
[13] N. M. Elfatih et al., “Internet of vehicle’s resource management in 5G networks using AI technologies: current status and trends,”
IET Communications, vol. 16, no. 5, pp. 400–420, Dec. 2021, doi: 10.1049/cmu2.12315.
[14] S. A. AlQahtani, “An efficient resource allocation to improve QoS of 5G slicing networks using general processor sharing‐based
scheduling algorithm,” International Journal of Communication Systems, vol. 33, no. 4, Nov. 2019, doi: 10.1002/dac.4250.
[15] J.-Y. Lin, P.-H. Chou, and R.-H. Hwang, “Dynamic resource allocation for network slicing with multi-tenants in 5G two-tier
networks,” Sensors, vol. 23, no. 10, May 2023, doi: 10.3390/s23104698.
[16] H. Beshley, M. Beshley, M. Medvetskyi, and J. Pyrih, “QoS-aware optimal radio resource allocation method for machine-type
communications in 5G LTE and beyond cellular networks,” Wireless Communications and Mobile Computing, vol. 2021, pp. 1–
18, May 2021, doi: 10.1155/2021/9966366.
[17] D. Sattar and A. Matrawy, “Optimal slice allocation in 5G core networks,” IEEE Networking Letters, vol. 1, no. 2, pp. 48–51, Jun.
2019, doi: 10.1109/lnet.2019.2908351.
[18] G. Arfaoui et al., “A security architecture for 5G networks,” IEEE Access, vol. 6, pp. 22466–22479, 2018, doi:
10.1109/access.2018.2827419.
[19] M. Condoluci and T. Mahmoodi, “Softwarization and virtualization in 5G mobile networks: benefits, trends and challenges,”
Computer Networks, vol. 146, pp. 65–84, Dec. 2018, doi: 10.1016/j.comnet.2018.09.005.
[20] P. K. Thiruvasagam, V. J. Kotagi, and C. S. R. Murthy, “A reliability-aware, delay guaranteed, and resource efficient placement
of service function chains in softwarized 5G networks,” IEEE Transactions on Cloud Computing, vol. 10, no. 3, pp. 1515–1531,
Jul. 2022, doi: 10.1109/tcc.2020.3020269.
[21] C. Song et al., “Hierarchical edge cloud enabling network slicing for 5G optical fronthaul,” Journal of Optical Communications
and Networking, vol. 11, no. 4, Mar. 2019, doi: 10.1364/jocn.11.000b60.
[22] O. Chabbouh, S. Ben Rejeb, N. Agoulmine, and Z. Choukair, “Cloud RAN architecture model based upon flexible RAN
functionalities split for 5G networks,” in 2017 31st International Conference on Advanced Information Networking and
Applications Workshops (WAINA), Mar. 2017, pp. 184–188, doi: 10.1109/WAINA.2017.107.
[23] X. Zhou, R. Li, T. Chen, and H. Zhang, “Network slicing as a service: enabling enterprises’ own software-defined cellular
networks,” IEEE Communications Magazine, vol. 54, no. 7, pp. 146–153, Jul. 2016, doi: 10.1109/mcom.2016.7509393.
[24] D. Sattar and A. Matrawy, “Towards secure slicing: using slice isolation to mitigate DDoS attacks on 5G core network slices,” in
2019 IEEE Conference on Communications and Network Security (CNS), Jun. 2019, pp. 82–90, doi: 10.1109/CNS.2019.8802852.
[25] N. Khumalo, O. Oyerinde, and L. Mfupe, “Reinforcement learning-based computation resource allocation scheme for 5G fog-
radio access network,” in 2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC), Apr. 2020,
pp. 353–355, doi: 10.1109/FMEC49853.2020.9144787.
[26] C. C. Sekhar, V. V, K. Vijayalakshmi, M. B. Sahaai, A. S. Rao, and S. Murugan, “Cloud-based water tank management and
control system,” in 2023 Second International Conference On Smart Technologies For Smart Nation (SmartTechCon), Aug.
2023, pp. 641–646, doi: 10.1109/SmartTechCon57526.2023.10391730.
[27] T. R. Saravanan, A. R. Rathinam, J. Lenin, A. Komathi, B. Bharathi, and S. Murugan, “Revolutionizing cloud computing:
evaluating the influence of Blockchain and consensus algorithms,” in 2023 3rd International Conference on Smart
Generation Computing, Communication and Networking (SMART GENCON), Dec. 2023, pp. 1–6, doi:
10.1109/SMARTGENCON60755.2023.10442008.
[28] M. J. Kumar, S. Mishra, E. G. Reddy, M. Rajmohan, S. Murugan, and N. A. Vignesh, “Bayesian decision model based reliable
route formation in internet of things,” Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), vol. 34,
no. 3, pp. 1665–1673, Jun. 2024, doi: 10.11591/ijeecs.v34.i3.pp1665-1673.
[29] M. Amru et al., “Network intrusion detection system by applying ensemble model for smart home,” International Journal of
Electrical and Computer Engineering (IJECE), vol. 14, no. 3, pp. 3485–3494, Jun. 2024, doi: 10.11591/ijece.v14i3.pp3485-3494.

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 14, No. 4, August 2024: 4721-4731
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BIOGRAPHIES OF AUTHORS
Nagarajan Suganthi received her B.Tech. in information technology from
Pindicherry Engineering College, Pondicherry University, India, in 2005 and an M.Tech in
information technology from Sathyabama University, Chennai, India, in 2008. She completed
her PhD in cognitive radio networks under computer science and engineering from
Sathyabama University, Chennai, India. She has 15 years of teaching experience at the college
level. Currently, she works as an assistant professor in the Department of Computer Science
and Engineering at SRM Institute of Science and Technology, Ramapuram, Chennai. She has
published around 13 papers at the International Conference and Journal. Her research interests
include cognitive radio networks, wireless communication, and machine learning. She can be
contacted at email: suganthn@srmist.edu.in.
Enthrakandi Narasimhan Ganesh has 28 years of teaching experience, of
which eight years were spent as principal and six years as dean. He is an M.Tech. graduate
from IIT Madras in microelectronics and VLSI design, and he has a PhD in nanotechnology
from JNTU Hyderabad with a Gold Medal. He has Executed 10 funded projects with five
patents granted. DST STTP Sponsored Funded one Crore project is in progress and about to be
submitted. He is guiding 13 Ph.D. students, out of which 8 have been awarded degrees. He has
60 Scopus-indexed international journals and 22 SCI journals (Annexure I), totaling 320
Publications to my credit. He has executed 12 Consultancy Projects for the cost of 60 Lakhs.
He has more than 50 International Conference Publications. Twenty best Conference paper
awards with distinguished faculty, researcher, and excellence in teaching awards, reviewer,
and editor for 8 international journals with six books published. He has academic, research,
and administrative experience. Also, Three times NAAC and NBA Committee External
member (accessor) and Interview Panel Member for DRDO and ISRO. He can be contacted at
email: enganesh50@gmail.com.
Elangovan Guruva Reddy received his B.E. degree from the University of
Madras in 1995, M.E. degree from CEG, Anna University, Chennai, in 2005, and Ph.D. degree
from MAHER, Chennai, in 2022. He has over twenty-five years of teaching experience in
various Universities and teaches postgraduate and undergraduate courses in wireless sensor
networks, ad-hoc networks, cloud computing, and IoT. He works as an associate professor in
the Department of Computer Science and Engineering at Koneru Lakshmaiah Education
Foundation, Vijayawada, AP, India. His research includes IoT, sensor networks, artificial
intelligence, cloud computing, and computer vision. He presented many papers at national and
international conferences. He is a member of IEEE, CSI, and life member in ISTE, and a royal
fellow member of the IOASD. He can be contacted at email: gurugovan@gmail.com.
Vijayaraman Balakumar is working as a sub-divisional engineer (security) at
MPLS Network Operations Centre, Bharat Sanchar Nigam Limited, Bengaluru, India. He
handles information and network security for MPLS networks on a Pan-India basis. He has
varied experience and expertise in next gen firewalls, big data security analytics, cyber
sentries, and cyber range simulations. He has a total of eighteen years of experience in the
telecommunications field. He has finished his bachelor of engineering in electronics and
communication engineering, master of business administration in systems management, and
doctorate in corporate customer attitude domain in management studies from Anna University,
Chennai. He has delivered several guest lectures and seminars in various domains, including
cyber security solutions, MPLS networks, telecom management, customer relationship
management, management practices, customer attitude, work ethics, database management,
network simulation, telecommunication switches, mobile communication, and career
guidance. He can be contacted at email: vinbalakumar@gmail.com.
Thangam Ilakkiya is currently working as an assistant professor in the
Department of Management Studies at Sri Sairam Institute of Technology, Chennai. She
completed her MBA from Anna University and is pursuing a Doctorate in management at
Annamalai University. She specializes in marketing and human resource management. She has
actively participated in and presented papers at both national and international conferences.
She has published in indexed journals and has a few patents in management stream. She can
be contacted at email: ilakkiya.mba@sairamit.edu.in.

Int J Elec & Comp Eng ISSN: 2088-8708 
Enhancing 5G network performance through effective resource management with … (Nagarajan Suganthi)
4731
Mageshkumar Naarayanasamy Varadarajan is an industry leader and
distinguished engineer with over 22 years of experience, captivated the audience with his
insights into software development, testing, and DevOps. Well versed with multiple
languages, solving critical issues, and leading teams, he spearheaded architectural changes for
large scale projects, modernized legacy systems to enhance efficiency and performance, and
defined cloud-agnostic solutions, all while receiving recognition for innovative solutions and
various successful project implementations. Mageshkumar demonstrates outstanding
knowledge in a variety of expertises including cloud and data security, and new technologies
like AI and machine learning. He can be contacted at email: magesh27@gmail.com.
Venkatachalam Ramesh Babu is currently working as a Dean of the University
Journals and professor of CSE at Dr. M.G.R. Educational and Research Institute. He has 27
years of experience in teaching and 4 years in the industry. He earned an M.E. in Computer
Science and Engineering and a Ph.D. in the same discipline. His broad field of research was
image processing. His areas of interest include IOT, machine learning, and big data. He has
published research papers in both international and national journals of repute. Besides his
academic stint. He has won many awards and accolades. He received the Distinguished
Technology Author Award from the National Trailblazers Triumph Award in 2023. He has
published a patent and a book on machine learning. He also serves as a member of the board
of studies. He is a member of technical societies like ISTE, CSTA, IAAC, IAENG, and
ICORSA. He has organized workshops and conferences at both national and international
levels. He has served as a session chair in conferences. He can be contacted at email:
rameshbabu.cse@drmgrdu.ac.in.