Implanting Intelligence in 5G Mobile Networks—A Practical Approach

Abstract

With the advancement in various technological fronts, we are expecting the design goals of smart cities to be realized earlier than expected. Undoubtedly, communication networks play the crucial role of backbone to all the verticals of smart cities, which is why we are surrounded by terminologies such as the Internet of Things, the Internet of Vehicles, the Internet of Medical Things, etc. In this paper, we focus on implanting intelligence in 5G and beyond mobile networks. In this connection, we design and develop a novel data-driven predictive model which may serve as an intelligent slicing framework for different verticals of smart cities. The proposed model is trained on different machine learning algorithms to predict the optimal network slice for a requested service resultantly assisting in allocating enough resources to the slice based on the traffic prediction.

Full text

Citation: Malik, S.; Khan, M.A.;
Aadam; El-Sayed, H.; Khan, J.; Ullah,
O. Implanting Intelligence in 5G
Mobile Networks—A
Practical Approach. Electronics 2022,
11, 3933. https://doi.org/10.3390/
electronics11233933
Academic Editor: Rameez Asif
Received: 12 October 2022
Accepted: 4 November 2022
Published: 28 November 2022
Publisher’s Note: MDPI stays neutral
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4.0/).
electronics
Article
Implanting Intelligence in 5G Mobile Networks—A
Practical Approach
Sumbal Malik 1,2 , Manzoor Ahmed Khan 1,2 , Aadam 1, Hesham El-Sayed 1,2,* , Jalal Khan 1
and Obaid Ullah 1
1
College of Information Technology, United Arab Emirates University, Abu Dhabi 15551, United Arab Emirates
2Emirates Center for Mobility Research (ECMR), United Arab Emirates University,
Abu Dhabi 15551, United Arab Emirates
*Correspondence: helsayed@uaeu.ac.ae
Abstract:
With the advancement in various technological fronts, we are expecting the design goals
of smart cities to be realized earlier than expected. Undoubtedly, communication networks play
the crucial role of backbone to all the verticals of smart cities, which is why we are surrounded by
terminologies such as the Internet of Things, the Internet of Vehicles, the Internet of Medical Things,
etc. In this paper, we focus on implanting intelligence in 5G and beyond mobile networks. In this
connection, we design and develop a novel data-driven predictive model which may serve as an
intelligent slicing framework for different verticals of smart cities. The proposed model is trained on
different machine learning algorithms to predict the optimal network slice for a requested service
resultantly assisting in allocating enough resources to the slice based on the traffic prediction.
Keywords: 5G; network slicing; machine learning; random forest; neural network
1. Introduction
As the world is transiting to digital cities, the issues with today’s networks become
a never-ending effort and are even getting more intense as networks evolve to cater for
the applications of low latency, high bandwidth, massive connectivity, support for high
mobility, etc. The complex and extremely high dynamic nature of the envisioned network
configuration asks for reduced human intervention. Programmable networks were one of
the initial attempts to remove humans from the equation. However, implanting intelligence
in the networks for complex use cases of different smart city verticals need more than just
achieving programmable networks. The features that 5G and beyond pledges provide are
the key ingredients. Machine Learning and artificial intelligence (AI) based approaches
further add to introducing human context and decision-making in the networks.
The telecom industry has been completely revolutionized by several developing
technologies that enable new business models and provide customers with new experiences.
The emergence of programmable systems such as Software Defined Networks (SDN) and
Network Function Virtualization (NFV) have evolved and benefited the networks. Among
the crucial services that 5G networks would encompass are autonomous driving, enterprise
business models, virtual reality solutions, industrial automation, remote monitoring, smart
health, smart cities, and many more. Network slicing (NS) is viewed as a major enabling
technology for 5G by the Third Generation Partnership Project (3GPP). It would enable
operators to effectively run several network instances over a single infrastructure to serve
various applications, use cases, and business services while providing the highest possible
quality of service (QoS).
NS, defined by Next Generation Mobile Network Alliance (NGMN) [
1
] is an important
component of 5G networks enabling the coexistence of multiple isolated and independent
virtual networks (slices) running on the same physical infrastructure. Each network slice is
an independent self-contained virtualized end-to-end network with its virtual resources,
Electronics 2022,11, 3933. https://doi.org/10.3390/electronics11233933 https://www.mdpi.com/journal/electronics

Electronics 2022,11, 3933 2 of 16
topology, traffic flow, and provisioning rules which allows the network operators to run
different deployments based on different architectures in parallel. Network slicing provides
multifold advantages such as (i) provides multi-tenancy therefore, multiple virtual network
operators can share the same physical infrastructure which reduces the capital expenses
of deploying and operating new networks; (ii) can create customized slices with varying
QoS (e.g., data speed, reliability, latency, delay, throughput) requirements for each service
meeting the service level agreements of the services; (iii) supports on-demand cost-effective
creation, modification and annul of slices increasing the flexibility and adaptability of the
network management [
2
]. However, some of the major challenges of NS are (i) dynamically
adapting application service quality requirements and (ii) unpredictable resource and
service quality demands. Furthermore, it is also worth highlighting that NS implementation
deals with routing issues in different settings. There are known challenges to routing [
3
],
and the research community have proposed various solution approaches to readers such as
interfere-aware routing [4], measurement-based routing, channel-diversity-based routing,
priority-based multi-path routing, and experience-driven model-free deep reinforcement
learning-based approaches [
5
], etc. On similar lines, the shortest path and route problem in
the network is one of the major challenges in the transportation and communication sectors.
In this connection, Karimnejad et al. [
6
] proposed a genetic, particle swarm optimization
algorithm to find the shortest path in a network with mixed fuzzy arc weights due to the
complexity of the addition of various fuzzy numbers for larger problems. Capri et al. [
7
]
also proposed a fuzzy-based Ant Colony Optimization algorithm to solve the shortest
path problems with different types of fuzzy weights. Sori et al. [
8
] implemented the elite
artificial bees’ colony algorithm to resolve the route planning challenges in robotics. In
another study, Sori et al., [
9
] also implemented an algorithm to solve the challenge of
finding a path with the lowest cost where the traversal time of the path does not exceed a
predetermined time-bound. For ready reference, the readers are also recommended to look
into the following research [
10
–
13
]. The enabling technologies to deploy network slicing
are SDN, NFV, and cloud computing discussed in Section 2.
This paper is organized into six sections. Section 2provides a tutorial to assist readers
with an overview of 5G enabling technologies and associated concepts. Section 3highlights
the importance of integrating the various enabling technologies and concepts that are
important to realize network slicing. Section 4discusses the potential ways towards 6G.
Section 5discusses the crucial challenges of network slicing and develops a predictive
model to predict the best network slice for a requested service. Finally, Section 6elucidates
the conclusion of the study.
2. Tutorial & Background
This section focuses on equipping the readers with the necessary background in-
formation on 5G enabling technologies, which is needed to comprehend the contents of
this paper.
2.1. ETSI NFV MANO Reference Architecture
The ETSI introduces the NFV MANO architecture [
14
] a fundamental base solution
to build the softwarized 5G networks. The 3GPP leverages this mechanism to create the
network slices enabling the dynamic deployment of network functions as virtual network
functions (VNFs) completely decoupled from the underlying hardware running these
functions. The VNF life cycle management together with dynamic resource allocation
to the network slices implemented as a set of interconnected VNFs are performed and
managed by ETSI NFV Management and Orchestration (MANO). MANO is mainly respon-
sible to manage and orchestrate the network services, VNFs, and other resources such as
networking, computation, and storage.
The ETSI NFV MANO architecture is comprised of three main building blocks:
•Management and Orchestrator (MANO):
responsible for managing and allocating
resources to NFV architecture such as creation and deletion of slices, slice selection

Electronics 2022,11, 3933 3 of 16
and resource allocation. The MANO architecture has three main components: (i)
Virtualized Infrastructure Manager (VIM): controls the physical and virtual infrastruc-
tures. Furthermore, it manages the interaction of a VNF with computing, storage and
network resources; (ii) VNF Manager: configure and manages the VNFs and takes care
of the life cycle of the VNFs. A VNF manager can be deployed for each VNF or a
single VNF manager can serve multiple VNFs; (iii) Orchestrator: manages the VIM and
the VNF manager.
•Virtualized Network Functions (VNFs):
VNF is the software implementation of net-
work functions deployed on one or more virtual machines running over the common
NFVI. A set of VNFs executed in a specific order is referred to as Service Function
Chain (SFC). The MANO can communicate with VNFs via the Ve-Vnfm connec-
tion point.
•NFV Infrastructure (NFVI):
a set of physical hardware and software resources build-
ing the environment to host and connect the VNFs. NFVI creates a virtualized en-
vironment for all the VNFs allowing them to communicate with the MANO via the
Nf-Vi connection point.
2.2. 5G Service Based Architecture
The 3GPP standardization body introduces the 5G core network architecture in the
technical specification (TS 23.501) as service-based architecture (SBA), where the elementary
module is the “Service” which defines a Network Function (NF). In SBA the architectural
elements defined as NFs offer their services via interfaces of a common framework to any
NFs that are allowed to make use of these provided services. The SBA realizes the flexible
addition and extension of functions including proprietary ones leveraging to connect to
other components without introducing specific new interfaces. The 5G SBA specified by the
3GPP is shown in Figure 1where several NFs are defined, such as SMF, AMF, UPF, etc. The
Authentication Server Function (AUSF) uses the Extensible Authentication Protocol (EAP)
for authentication; the Access and Mobility Management Function (AMF) is responsible
for hosting all mobility management-related functionality and terminates the non-access
stratum (NAS) and N2 reference point messages. The NAS messages are sent between the
UE and the AMF for mobility management and between the UE and Session Management
Function (SMF) for session management. The Network Slice Selection Function (NSSF)
selects the set of network slice instances to serve the UE; the Policy Control Function (PCF)
provides policy rules to the control pane functions; and the SMF controls the User Plane
Function (UPF) to manage the session (e.g., session establishment, modify, and release).
The Unified Data Management (UDM) stores the subscription and authentication data, and
the UPF is the user plane gateway serving the UE by connecting the RAN nodes to the data
network (DN). The Application Function (AF) enables dynamic policy and charging control
for applications. The UE is the mobile terminal and the DN may be operator services,
internet access, or third-party services [15].
Under this architecture, real-time communication between each NF is greatly guaran-
teed through the central service bus connection and the communication path of each NF
service can be optimized according to the demand. The communication between the NF
service is realized through REST programming application interface (API) calls using a
standard HTTP/2 routing mechanism. The HTTP/2 over Transmission Control Protocol
(TCP) is used as the protocol to invoke the API(s). However, the access elements in the
network such as Radio Access Network (RAN), UE, and NFs transferring the user plane
communication still use the dedicated reference points.

Electronics 2022,11, 3933 4 of 16
PCFNSSF NEF NRF NWDAF UDM
Service Based Interfaces
AMF SMF AUSF UDR AF
Control Plane
UE RAN UPF DN
N1 N2
N3
N4
N6
User Plane
Figure 1. 5G Service Based Architecture.
The three main components of an SBA service framework are service registration,
discovery, and authentication. To execute the service framework, the Network Repository
Function (NRF) is introduced. A new service registers with NRF by providing the relevant
information such as the type of service and network address characteristics etc. In this
way, the service acts as a service provider, allowing it to be discovered anytime it is needed.
Service consumers are those who use a particular service. Therefore, the service discovery
method allows a service consumer to find the right service provider while the authentication
system ensures that only those who have been granted access to the service can use it [
16
].
2.3. Network Data Analytics Function
The 3GPP (TS 29.520) introduces relatively a new Network Data Analytics Func-
tion (NWDAF) in the 5G SBA as a core network function that supports intelligent and
autonomous network operations and service management. NWDAF is a general archi-
tecture through which various data-driven artificial intelligence (AI) / machine learning
(ML) analytical technologies can be integrated with the 5G networks. Figure 2shows the
NWDAF architecture where it collects the data from different modules such as network
functions e.g., (AMF, SMF, and PCF) application functions (AFs), the unified data repository
(UDR), and operation, administration, and management (OAM) system to perform the
network analytics.
NFs
AF
Network Data Analytics
Function (NWDAF)
Input: Delivery of activity
data and local analytics
Data Access
Network OAM
System
NFs
AF
UDR
User Repositories
Analytics
Model
Data Access
Output: Delivery of
analytics data
Figure 2. Architecture of Network Data Analytics Function.
Typically, the data are collected from the NFs when the data are related to the indi-
vidual UE or network sessions. However, the data related to the global network states,
such as performance measurements of network slices and services/applications, are often
obtained from OAM. NWDAF can collect the data in two ways either reactively requests
the particular customer (NF/AF/OAM) for a certain type of information or proactively

Electronics 2022,11, 3933 5 of 16
retrieves the data for long-run analytics. The NWDAF realizes the data collection via a
service-based interface (SBI) using either the request/response mode to collect the data
for one time or subscription/notification mode to regularly retrieve the data for a certain
time period. Once the data are collected, it performs the analytics and delivers this ana-
lytics information to the requested NFs, AF, and OAM to make optimal decisions about
network operations and management actions. The NWDAF generates two main types
of analytical information (i) statistical information and (ii) prediction information [
17
].
The statistical information includes traffic load and resource utilization of network slices,
network/service performance measurements, and user mobility pattern analysis. However,
the prediction information comprises temporal and spatial traffic distribution predicted
for a future time period and UE location prediction for a future time instant. The 5G SBA
allows the NWDAF to be deployed as single or multiple virtual network functions (VNF)
on the same network domain. The multiple instances of NWDAF can be deployed as either
a central NF, a collection of distributed NFs, or a combination of both. When multiple
instances of NWDAF are deployed, each instance may provide a certain type of analytics.
The interaction between the NWDAF and the NFs is a consumer-provider model. That is,
5G SBA allows the consumer of the NWDAF to discover and select the suitable NWDAF
instance to obtain the specific analytics via the network NRF. After discovering an NWDAF
instance, the consumer NFs can either retrieve the data via SBI request/response messaging
protocol to obtain one-time analytical information per request or may subscribe to a certain
type of analytic service offered by the NWDAF instance to regularly receive notification
messages from the NWDAF instance that delivers analytical information generated from
the service.
Concludingly, some of the main use cases of 5G NWDAF are: (i) load-level com-
putation and prediction for a network slice instance; (ii) load analytics information and
prediction for a specific NF; (iii) network load performance computation and future load
prediction; (iv) UE mobility-related information and prediction; (v) QoS sustainability
which involves the reporting and prediction of QoS change [18].
3. Integration of the Enabling Technologies
In this section, we highlight the need for integration of the various enabling tech-
nologies and concepts that are instrumental in realizing the concept of network slicing
by exploiting the architectural components of 5G’s SBA. We believe an analysis of the
renowned 5G platforms would enable the readers to not only comprehend the said inte-
gration but also understand the differentiating features of each platform. Furthermore, we
also believe that the contents of this section will provide a quick hands-on start for the
researchers in this domain. In what follows next, we introduce and compare various 5G
RAN, Core and Network Functions Virtualization Orchestrator platforms carefully. The
comparison is provided in Tables 1–3respectively. However, the readers are encouraged to
look into the platforms’ documentation for more details.
3.1. Radio Access Network Platforms
The comparison between the RAN solutions/platforms is based on eight factors
such as the support for eNodeB (eNB), gNodeB (gNB), Multiple Input Multiple Output
(MIMO), Software Defined Radio (SDR) user equipment, Commercial Off-the-Shelf (COTS)
user equipment, type of license, main contributors, and type of community support. The
comparison shown in Table 1shows that the OAI RAN and srsLTE provide both the imple-
mentation of eNB and gNB whereas the Radisys RAN only provides the implementation
of gNB. Radisys contributes to O-RAN providing an open-source implementation of the
3GPP new radio stack for the gNB distributed unit. O-RAN is open, intelligent, and de-
ployed on a virtualized platform with high flexibility. The concept behind the O-RAN is
all about disaggregating the hardware from software: open hardware and software using
the standard processors with open interfaces. All RANs, listed in the table support the
MIMO technology for the uplink and downlink. Furthermore, the OAI RAN and srsLTE

Electronics 2022,11, 3933 6 of 16
can be used to serve both COTS and SDR UEs, however, this information is not available
for the Radisys RAN. In terms of the license, the OAI RAN is distributed under the Apache
License v1.1 which enables individuals and companies who have patents to contribute
to the OAI source code while maintaining their patent rights. The Radisys is distributed
under the Apache v2.0 license and the srsLTE is written in C++ and released under the
GNU AGPLv3 license.
Table 1. Comparison of Radio Access Network Platforms.
Features
Category Platforms/
Framework Ref. eNB gNB Support for
MIMO
Support for
SDR UE
Support for
COTS UE Licence Main
Contributor
Community
Support
OAI RAN [19] Yes Yes Yes Yes Yes OAI public
license v1.1
OAI software
alliance,
EURECOM
Mailing List
Radisys open
source RAN [20] No Yes,
(O-RAN) Yes - N/A
Apache v2.0,
O-RAN Software
License v1.0
Radisys No
Radio Access
Network
srsLTE [21] Yes Yes Yes Yes Yes GNU AGPLv3 Software radio
systems Mailing List
3.2. Core Network Platforms
In this sub-section, the main solutions to deploy the core network are discussed briefly.
The comparison between the core solutions/platforms is based on seven factors such
as the operational mode, support for core functions/components, supported interfaces,
network slice, type of license, main contributors, and type of community support. The
comparison shown in Table 2shows that OAI-CN and free5GC projects focus only on
the development of the 5G Standalone (SA) operational mode, however, the Open5GS,
Open5GCore, SD-Core, Magma Core, and Nokia 5G core implement both 5G SA and 5G
Non-Standalone (NSA) modes. In addition to this, some of these platforms also provide
support for 4G, LTE, and NB-IoT LTE modes. In terms of network functions, most of
the platforms can deploy AMF, SMF, AUSF, UDM, UDR, NRF, etc functions. However,
OAI-CN, free5GS, Open5GCore, and Nokia 5G Core platforms can also deploy the newly
introduced Non-3GPP Interworking Function (N3WIF) which is responsible to interwork
between untrusted non-3GPP networks and the 5G Core. It acts as a gateway for the
5G core network providing the support for N2 and N3 interface towards the 5G core.
Additionally, N3IWF also provides a secure connection for the user equipment accessing
the 5G core over a non-3GPP access network with support for internet protocol security
(IPsec) between the UE and the N3IWF. Magma Core can only deploy the AMF function
since the remaining functions are still under development. Considering the importance of
intelligence in 5G core networks, it is important to highlight that only the Nokia 5G Core
platform can deploy the NWDAF function which supports intelligent and autonomous
network operations and service management. On the other hand, most of the platforms
listed in Table 2bring great convenience to the implementation of network slicing such as
OAI-CN, free5GC, Open5GCore, etc. Finally, in terms of the license, the OAI-CN, free5GC,
and COMAC are distributed under the Apache V2.0 license, Open5GS is under the GNU
AGPL3 license, Magma Core is under Berkeley Source Distribution (BSD) license, and the
SD-Core is released with an ONF member-only license.

Electronics 2022,11, 3933 7 of 16
Table 2. Comparison of 5G Core Network Platforms and Framework.
Features
Category Platforms/
Framework Ref. Operational
Mode
Functions/
Components
Supported
Interfaces
Network Slice
Support
Supported
Operating System License Main
Contributors
Community
Support
OAI-CN [22] 5G SA
AMF, SMF, NRF,
AUSF, UDM, UDR,
N3IWF, and UPF
- Yes
Ubuntu
14.04 LTS and
Ubuntu 16.04
Apache v2.0
OpenAirInterface
software alliance,
EURECOM
Mailing List
free5GC [23] 5G SA AMF, SMF,UPF,
OAM, N3IWF, UDM
N1/N2, N3/N4/N6
N8, N10/N11,
N12/N13
Yes Ubuntu
18.04 Apache v2.0 Free5GC Forum
Open5GS [24]5G NSA Core,
5G SA
AMF, SMF, AUSF,
UDM, NRF, UPF - -
Ubuntu, openSUSE,
CentOS, Fedora, and
Mac OS
GNU AGPLv3 Open5GS Mailing List/
Forum
Open5GCore [25]5G SA, 5G NSA,
LTE, NB-IoT LTE
AMF, SMF, AUSF,
UDM, NRF, UPF,
UDR, BSF, N3IWF
5G Interfaces
(N1, N2, N3, N4) Yes - Paid
License
Fraunhofer
FOKUS
Tutorials
and training
sessions
SD-Core [26]LTE, 5G NSA
and 5G SA
AMF, SMF, AUSF,
PCF, NRF, UDM,
UDR, NSSF
- Yes - Member-Only
Software License ONF Mailing List
Magma Core [27]
4G, NB-IoT
Support, 5G NSA,
5G SA
AMF
* Remaining functions
are under development
N1,N2,N3 Yes Mac OS,
Ubuntu 20.04 BSD Facebook Mailing List/
Forum
COMAC [28] - - - - - Apache v2.0 ONF Mailing List
Core
Network
Nokia
5G Core [29] 5G NSA, 5G SA
UPF, N3IWF, NEF,
NRF, NWDAF, PCF,
SMF, AUSF, UDM,
UDR
- Yes - - Nokia Training
3.3. Network Functions Virtualization Orchestrator Platforms
In this sub-section, the well-known platforms to deploy NFV Orchestration are dis-
cussed briefly. The comparison between the platforms is based on nine factors, such as
Compliance with ETSI MANO architecture, support for external APIs, type of network
services, components/frameworks, infrastructure, supported VIM types, community, and
installation prerequisites. The comparison shown in Table 3shows that the MANO OSM,
Cloudify, and Open Baton are fully-compliant with the ETSI NFV MANO architecture,
however, the ONAP provides partial compliance with this architecture. The OSM commu-
nity is compliant with the MANO reference architecture, following the IFA working group
specifications. Key reference points, such as Or-Vnfm and Or-Vi might be identified within
OSM components. Furthermore, the VNF and network service catalogue are explicitly
available in an OSM service orchestrator component. ONAP, OSM, and Cloudify frame-
works are equipped with APIs offered to OSS/BSS and other relevant services, however,
the Open Baton provides access to Java SDK external API. In terms of network service and
support for network slicing; all platforms listed in the Table provide the VNFs service and
the ONAP, OSM, and Open Baton also support network slicing at the infrastructure level.
All these frameworks leverage the virtual machine, and containers with Kubernetes, and
Docker to deploy the platforms. Some of the other comparison factors along with platform
installation prerequisites are discussed in Table 3.
Table 3. Comparison of NFV Orchestration Platforms.
Features Minimum Installation Prerequisites
Category Platforms Ref. Compliance
w/ETSI
MANO
External APIs Network
Service
Support for
Network
Slicing
Components/
Frameworks Infrastructure Supported
VIM Types Community CPUs/
vCPUs RAM Storage
Supported
Operating
System
ONAP [30] Partially
REST APIs
(for external controllers,
OSS/BSS, etc.)
VNFs,
PNFs Yes
(i) Management Framework
(ii) Design Time Framework
(iii) Run Time Framework
Virtual machine,
Containers w/Kubernetes
& Docker
OpenStack
Kubernetes
Vmware
Azure
Linux foundation
w/telecom operators
112
vCPUs 224 GB 160 GB Ubuntu 14.04
Ubuntu 16.04
MANO
OSM [31] Yes
REST APIs (for
external controllers,
OSS/BSS, etc.)
VNFs,
PNFs Yes (i)Information Model
(ii) OSM Automation Framework
Virtual machine,
Containers
w/Kubernetes
OpenStack
Vmware
OpenVIM
Kubernetes
ETSI
w/telecom operators 2 CPUs 6 GB 40 GB Ubuntu20.04
Cloudify [32] Yes
Asynchronous RESTful
API/Users &
Services authorization
VNF N/A -VMs/Containers OpenStack Cloudify
Team
2
vCPUs 4 GB 5 GB
Ubuntu 14.04
CentOS 7.1
OS X 10.11
Virtual Network
Function Orchestrator
Open Baton [33] Yes Java SDK VNFs Yes -Virtual machines,
Containers w/Docker OpenStack
Fraunhofer
FOKUS &
TU Berlin
2 CPUs 2 GB 10 GB Ubuntu 14.04
Debian Jessy
4. Potential Way Forward to 6G
The stakeholders are figuring out 6G design objectives as 5G is nearly at the point of
technological realization in all of its features. To successfully operate internet of everything

Electronics 2022,11, 3933 8 of 16
(IoE) services such as extended reality (XR) and networked autonomous systems, a wireless
system must concurrently deliver high reliability, low latency, and high data rates, for
heterogeneous devices, across uplink and downlink. Additionally, end-to-end co-design of
computation, control, and communication functionalities will be necessary for emerging
IoE services, which have been mostly neglected up until now. To support this new class of
services, specific challenges must be overcome, such as characterizing the fundamental rate-
reliability-latency tradeoffs that govern their performance, exploiting frequencies above
sub-6 GHz, and converting wireless systems into a self-sustaining, intelligent network fabric
that flexibly provisions and orchestrates communication-computing control-localization-
sensing resources tailored to the required IoE scenario [34].
The various new use cases that we anticipate by 2030 and beyond will determine the
new standards that 6G must meet. The data rate/throughput/capacity, latency, reliability,
scale, and flexibility key performance indicators (KPIs) from 5G will continue to be crucial
benchmarks for 6G performance. For 6G’s anticipated use cases, several additional features
will also be crucial, including (i) dynamic digital twins and virtual worlds; (ii) zero-energy
devices; and (iii) wireless in data centres, etc [
10
]. In Figure 3, we categorise the require-
ments for 6G into six categories three of which have KPIs identical to those of 5G and three
of which are the new ones.
Figure 3. Key requirements and characteristics of 6G.
6G is also anticipated to address the difficulties unique to a space-air-ground integrated
network (SAGIN). An integrated mobile communication network called SAGIN combines
satellites, airborne platforms and terrestrial networks. The terrestrial networks are capable
to provide standardized coverage of urban hotspots. In remote locations, on the sea, and in
the air, satellite and airborne technology provide ubiquitous coverage. SAGIN is a deep
integration of a system, technology, and application rather than merely connecting various
communication networks. By integrating the RAN and CN, SAGIN creates a unified control
plane and data plane, as well as a uniform terminal and air interface protocol. A robust
control plane is developed by leveraging the deployment of the control network element.
Moreover, to achieve seamless handover, intelligent mobility management technology is
also deployed. Differentiated micro-service network elements with space-air characteristics
are used to accomplish the space-air QoS provisioning, policy routing, resource allocation,
and other network operation management activities.
Despite the conceptual infancy of SAGIN, relevant stakeholders have provided several
important insights into the improvement of terrestrial, aerial, and satellite systems. SAGINs
are anticipated to cover a wider range of smart city scenarios, which present enormous and
constantly changing diversified service requirements. This encourages the development
of the IMT2020 usage case scenarios into further-eMBB (feMBB), ultra-mMTC (umMTC),
extremely reliable and low latency communications (ERLLC), mobile broadband reliable
low latency communications (MBRLLC), and massive-uRLLC (muRLLC). This is to say that
factors such as network resource scheduling, round trip duration, throughput, handover
management, etc. are essential for a higher level of autonomous driving. Even though

Electronics 2022,11, 3933 9 of 16
centralized broadcast scheduling guarantees excellent broadcast reliability, it is burdened
by signalling overheads caused by dynamically changing vehicle positioning and network
resource demands. Therefore, the distribution version of resource scheduling implanted
with autonomous behaviour provides improved scalability, which is also reflected in en-
hanced sidelink transmission, carrier aggregation, 5GNR, support for unicast and multicast,
etc. of 3GPP Release 15 and 16 [
35
]. The improvement in URLLC, positioning precision,
and reliability over 6G is further aided by 3GPP Release 17. The pervasive network intel-
ligence made possible by 6G is anticipated to offer greater service diversity and flexible
service configuration. AI-enabled solutions for service composition can address issues
with service-oriented networks, such as huge data exploitation through the use of big data
approaches and service identification through intent-driven networking.
Zhang et al. [
36
] investigate the support for vehicle networks in the envisioned
integrated space-air-ground and emphasize the need for integrated communication to
support the requirements of various vehicular services, particularly in cases of frequent
handovers. The authors highlighted a variety of challenges preventing the implementation
of such integrated communication systems, including those of heterogeneous management,
dynamic networking, service quality, etc. In another, research the research literature survey
of 6G SAGINs from a service-oriented network perspective was carried out by Cheng
et al. [
37
]. The authors provide a brief overview of the various SAGIN types, which
include Cooperative satellite-terrestrial networks, Cognitive satellite-terrestrial networks,
Hybrid satellite-terrestrial relay networks, and Satellite-terrestrial Backhaul Networks.
The advantages of SAGINs for autonomous driving were also underlined by the authors,
including LEO’s low cost and ubiquitous connection, the wide carrier coverage that reduces
handovers, caching of the pertinent contents in UAVs, etc.
5. Dynamic Network Slicing—A Dilemma for Highly Changing Environment
Now that the reader is equipped with the necessary background and platforms, in this
section, we highlight a few crucial challenges of network slicing that this study addresses:
•
Challenge 1—Dynamically adapting application service quality requirements: As the world
transits to adopting new solutions in different verticals e.g., remote surgery in eHealth,
autonomous driving in transportation, smart homes, smart farms, etc, the service
quality for network relevant service are subject to frequently changing owing to highly
dynamic environments. Hence, a network slice for a longer period may not fall under
optimal solutions.
•
Challenge 2—Unpredictable Resource and Service Quality Demands: The classical method
of resource allocation, bearer formation (e.g., in LTE), and network slicing are driven
by demand estimation, which has been relatively deterministic owing to the type
of services being used so far. However, services of the future are expected to ask
for dynamically changing network slices on shorter time quanta, which is usually
the consequence of a fast-changing environment. The obvious challenge here is the
accurate demand estimation.
To address the above challenges, this study proposes the concept of a data-driven
proactive network slicing mechanism detailed below.
This research aims to build a predictive model to predict the optimal network slice
for a requested service resultantly assisting in allocating enough resources to that slice
based on the traffic prediction. The big picture of data-driven network slice prediction
is shown in Figure 4. The proposed methodology is comprised of the following steps;
(i) the network slice data are downloaded, provided by Anurag et al. [
38
]; (ii) the data
are pre-processed and cleaned to build models; (iii) the cleaned dataset is then, divided
into training and testing sets; (iv) the predictive models are developed for three machine
learning algorithms to predict the network slice type; (v) testing data are then given to
the model to test the model performance; (vi) finally, the results of all the algorithms are
evaluated and compared.

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Figure 4. Big picture of Data-Driven Network Slice Prediction.
5.1. Dataset
In this research, the DeepSlice dataset published by Anurag et al. [
38
] is used to predict
the optimal network slice for all incoming requests. The dataset is comprised of 63,168
instances and 8 features. Some of the main features and their possible values are discussed
in Table 4.
Table 4. An Overview of DeepSlice Dataset.
Feature Values
Use case
Type
Smartphone, IoT Devices, Smart Transportation, Industry 4.0,
AR/VR/Gaming, Healthcare, Smart City/Home
User Equipement
Category LTE, 5G
Technology
Supported LTE/5G, IoT (LTE-M, NB-IoT)
Guaranteed Bit
Rate (GBR) GBR, Non-GBR
Reliability 0.01, 0.001, 0.000001
Latency (Ms) 10, 50, 60, 75, 100, 150, 300
5.2. Preprocessing
Data preprocessing and cleaning are the most important stages to handle the data
before using them in machine learning algorithms. Without preprocessing, the raw data
cannot be transformed into a useful and efficient format to create a machine-learning model.
The DeepSlice dataset is available in CSV format. After downloading the dataset, we
remove the duplicate instances. After then, we perform the attribute value transformation
from string to numeric values.
5.3. Data Splitting
The cleaned dataset is then split into 80% training and 20% testing sets. To keep the
target class “slice type” balanced, its distribution should be the same in both training and
testing datasets ensuring a more balanced dataset instead of a skewed one. Therefore, we
equally distributed the data.

Electronics 2022,11, 3933 11 of 16
5.4. Machine Learning Algorithms for Slice Prediction
The training data (80%) is given to machine learning algorithms to train the model.
We build the classification models, and the slice type attribute is used as the target variable.
Three machine learning algorithms: Random Forest (RF), eXtreme Gradient Boosting
(XGBoost) and Neural Network (NN) are trained, and compared to classify the slice type
of a request into eMBB (enhanced Mobile Broadband), URLLC (Ultra Reliable Low Latency
Communications), mMTC (massive Machine Type Communications), and master slice
based on the following parameters such as throughput, latency, equipment type, reliability,
mobility, etc. RF is a supervised learning model mostly used to construct predictive
models for both regression and classification problems. The reason behind choosing RF
over the other algorithms such as Decision Tree or Naïve Bayes is that we leveraged
the well-structured data, therefore, the RF uses several sub-trees to lessen the chance
of overfitting. The RF works well with large datasets and outputs accurate predictions.
Moreover, it is capable of determining the missing values and maintaining the accuracy of
the model even if some input data are missing. XGBoost is a branch of Gradient Boosting
Machine (GBM) techniques used to build both classification and regression predictive
models. XGBoost is an ensemble approach, where new models are built to eliminate
residuals or other errors from earlier models before being integrated to provide the final
prediction. Experimentally, XGBoost outperforms many ensemble classifiers in terms of
speed and performance. Lastly, a three-layer feed-forward neural network (FNN) with a
ReLu activation function is implemented to predict the slice. The selected parameters used
to train the classifiers are shown in Table 5. After training the classifiers the testing data
(30%) are given to the model to predict the slice type.
Table 5. Selected Parameters for Algorithms.
Algorithm Parameters
Random Forest Batch size = 100; Iterations = 100; Seed = 1
XGBoost Default Parameters
Neural Network Epochs = 5; Batch size= 16; Learning Rate = 0.003 Dropout = (p = 0.2);
Optimizer = Adam; Loss = CrossEntropyLoss
5.5. Experimental Results and Discussion
The experiments are conducted on Windows 10 operating system and Python version
3.7. The multi-class confusion metric [
39
] is used to evaluate the performance of the
algorithms where the experimental results show that both RF and XGBoost give significantly
good accuracy to predict the slice for all incoming requests to the network. The logic behind
the random forest is the bagging principle, which improves the overall performance by
adding more randomness to the model as the trees grow, which; resultantly contributes to
achieving high accuracy. Besides the good performance of random forest, it also takes less
time to build and run the model.
Interpretability refers to the ability to explain what the model is doing; it is a crucial
step in the ML pipeline. Therefore, to increase the understanding of our model we leveraged
the feature importance concept. Another reason to choose the feature importance is to help
us to determine whether the predictions made by the model are sensible. Figure 5and 6
show the feature importance of both algorithms. The feature importance is referred to as
an assessment of the specific contribution made by the relevant feature for a given classifier
regardless of the shape or orientation of the feature effect. This is to say that the features
of the input data have varying degrees of importance depending on the classification
model used and that a feature that is significant for one model may not be significant for
another [
40
]. Therefore, it is imperative to determine the degree of usefulness of a specific
variable for a current model and prediction. Figure 5clearly shows that, with RF, the
technology supported, latency, use case type, and reliability features are more significant.

Electronics 2022,11, 3933 12 of 16
On the other hand, in Figure 6, XGBoost gives more significance to the use case type,
GBR, latency and technology-supported features respectively to train and test the model.
These results indicate that GBR, latency and reliability requirements play an important
role in accurately inferring the slice types. Furthermore, the Figures highlight interesting
insight that both algorithms assign zero importance to day and time features showing no
importance of these features in training the models.
For ML classification problems a state-of-the-art way to evaluate the model’s perfor-
mance is to compute its accuracy. Therefore, to further evaluate the effectiveness of the
proposed model we leveraged the accuracy metric. In the proposed research the accuracy
aims to determine the capability of the model to accurately predict the slice type. Figure 7
shows the training and testing accuracy of the neural network and it is evident from the
result that the model achieved significant accuracy.
0 5 10 15 20 25 30 35 40 45
Technology Supported
Latency
Use Case Type
Reliability
GBR
LTE/5G UE
Time
Day
Value %
Features
Random Forest
Figure 5. Feature Importance of Random Forest Algorithm.
0 5 10 15 20 25 30 35
Technology Supported
Latency
Use Case Type
Reliability
GBR
LTE/5G UE
Time
Day
Value %
Features
XGBoost
Figure 6. Feature Importance of XGBoost Algorithm.

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Epoch
Accuracy
0
25
50
75
100
0 5 10 15 20
Training
Testing
Model Accuracy
Figure 7. Training and validation accuracy of Neural Network.
6. Conclusions and Future Work
This paper is mainly divided into four parts. The first part provides an overview of
5G enabling technologies and network slicing to comprehend the content of this paper. The
second part highlights the need to integrate the enabling technologies that are important
in realizing the concept of network slicing by exploiting the architectural components
of 5G’s SBA. Furthermore, it equips the reader with an overview of 5G platforms. The
fourth section discusses the potential ways towards 6G. The fifth section provides the
methodology and the results of the initial deployment of 5G network slicing leveraging
the machine learning algorithms. A predictive classifier is developed to predict the slice
for each incoming request to the network. Three machine algorithms (random forest,
XGBoost and neural network) are used to deploy the model and the experiments show
significantly promising results. The limitation of the proposed work is that the model is
trained on a set of data which may be changed in real-time scenarios, hence the accuracy
may fail. We are digitalizing the roads of the United Arab Emirates University making them
smarter by deploying smart towers equipped with heterogeneous sensors, communication
infrastructure, and AI toolbox. In this connection, the proposed framework will be used to
deploy the 5G core to implement various use cases such as autonomous driving and ehealth.
Moreover, we also plan to completely automate the proposed network slicing framework
by leveraging the NWDAF function and the 5G platforms discussed in the paper.
Author Contributions:
Conceptualization, S.M., M.A.K., A., Investigation, S.M., M.A.K., A. and
H.E.-S.; Methodology, S.M., M.A.K. and A., H.E.-S.; Project administration, S.M., J.K. and O.U.;
Supervision, M.A.K. and H.E.-S.; Writing—original draft, S.M.; M.A.K.; Writing—review and editing,
S.M., M.A.K., A., H.E.-S., J.K. and O.U. All authors have read and agreed to the published version of
the manuscript.
Funding:
This research was funded by the Emirates Center for Mobility Research (ECMR) of the
United Arab Emirates University (grant number 31R151).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.

Electronics 2022,11, 3933 14 of 16
Abbreviations
The following abbreviations are used in this manuscript:
3GPP 3rd Generation Partnership Project
5G Fifth Generation
6G Sixth Generation
AF Application Function
AI Artificial Intelligence
AMF Access and Mobility Function
AUSF Authentication Server Function
BSS Business Support Systems
COTS Commercial-Off-The-Shelf
DN Data Network
eMBB enhanced Mobile Broadband
eNB eNodeB
ETSI European Telecommunications Standards Institute
FNN Feedforward Neural Network
gNB gNodeB
HTTP/2 Hypertext Transfer Protocol (Second Version)
LTE Long Term Evolution
MANO Management and Orchestrator
MIMO Multiple-Input Multiple-Output
ML Machine Learning
mMTC massive Machine Type Communications
N3WIF Non-3GPP Interworking Function
NB-IoT NarrowBand-Internet of Things
NFVI Network Function Virtualization Infrastructure
NF Network Function
NFV Network Function Virtualization
NRF Network Repository Function
NSA Non-Standalone
NSSF Network Slice Selection Function
NWDAF Network Data Analytics Function
O-RAN Open Radio Access Network
OAM Operation, Administration, and Management
ONF Open Networking Foundation
OSS Operations Support Systems
PCF Policy Control Function
QoS Quality of Service
RAN Radio Access Network
RF Random Forest
SDR Software-Defined Radio
SBA Service Based Architecture
SBI Service Based Interface
SMF Session Management Function
SA Standalone
UDR Unified Data Repository
UE User Equipment
URLLC Ultra Reliable Low Latency Communications
VNF Virtual Network Function
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