TOPIC - AI-DRIVEN OPTIMIZATION & SELF-HEALING FOR 5G NETWORKS
Enrollment No. –
2O1O2O59
Name of Student –
Aditi Aggarwal
Name of Supervisor – Dr. Pankaj Kumar Yadav (Assistant Professor in the dept. of ECE)
November 2023
Submitted in partial fulfilment of the Degree of
Bachelor of Technology
DEPARTMENT OF ELECTRONICS & COMMUNICATION ENGINEERING
JAYPEE INSTITUTE OF INFORMATION TECHNOLOGY, NOIDA (U.P)
[1]
TABLE
OF CONTENTS
[2]
CERTIFICATE
This is to certify that the work titled “AI-DRIVEN OPTIMIZATION & SELF-HEALING
FOR 5G NETWORKS” submitted by Aditi Aggarwal in partial fulfilment for the award of
degree of B.Tech of Jaypee Institute of Information Technology, Noida has been carried out
under my supervision. This work has not been submitted partially or wholly to any other
University or Institute for the award of this or any other degree or diploma.
.
(Signature of Supervisor)
Name of Supervisor : Dr. Pankaj Kumar Yadav
Designation :
Assistant Professor in dept. of ECE
Date :
30 – 11 - 2023
[3]
ACKNOWLEDGEMENT
It gives me immense pleasure to express our deepest sense of gratitude and sincere thanks to
our highest respected and esteemed guide Dr.Pankaj Kumar Yadav for his valuable guidance,
encouragement and help for completing this work. His useful suggestions for this whole work
and co-operative behaviour are sincerely acknowledged. I would like to express my sincere
thanks to her for giving me this opportunity to undertake this project. I would also like to
express my indebtedness to my parents as well as my family members whose blessings and
support always helped me to face the challenges ahead.
Name of Student : Aditi Aggarwal
Enrollment No. : 20102059
Date :
30 – 11- 2023
[4]
ABSTRACT
Massive multiple-input multiple-output antenna systems, millimeter wave communications,
and ultra-dense networks have been widely perceived as the three key enablers that facilitate
the development and deployment of 5G systems. This article discusses the intelligent agent that
combines sensing, learning, and optimizing to facilitate these enablers. We present a flexible,
rapidly deployable, and cross-layer artificial intelligence (AI)-based framework to enable the
imminent and future demands on 5G and beyond. We present example AI-enabled 5G use cases
that accommodate important 5G-specific capabilities and discuss the value of AI for enabling
network evolution.
(Signature)
Name Of Student : Aditi Aggarwal
(Signature)
Name of Supervisor : Dr. Pankaj Kumar Yadav
Date : 30-11-2023
Date: 30-11-2023
[5]
LIST OF SYMBOLS & ACRONYMS USED :
[6]
CHAPTER 1 – INTRODUCTION :
1.1 GENERAL INTRODUCTION –
After the initial configuration, a continuous monitoring of the system parameters is
required to keep the communication system efficient and error-free. At this point, the
concept of self-optimization plays a vital role by providing autonomous network
optimization through AI/ML algorithm systems, requiring minimal external intervention.
For example, HO management, energy efficiency, radio resource management, and load
balancing are some of the domains that necessitate self-optimization and behavioural
(e.g., different behaviours of users at different dates and times) changes, optimization is a
never-ending process. However, the important point here is that while time-consuming
conventional approaches would render the network optimization task highly inefficient, it
could be made a lot easier and very efficient via automatic action sets driven by AI/ML
algorithms.
1.2 PROBLEM STATEMENT –
5G networks, while offering remarkable benefits, introduce complexities in terms of
network management, resource allocation, and maintenance. The diverse range of
applications, varying data traffic patterns, and real-time demands necessitate a dynamic
and intelligent approach to network optimization and management. Ensuring seamless
connectivity, low latency, and high throughput across diverse use cases presents a
considerable challenge, requiring constant adjustments to network parameters and prompt
identification of issues that may hinder performance.
Traditional manual network management approaches are inadequate for the dynamic
nature of 5G networks. Relying solely on human intervention to configure parameters,
troubleshoot anomalies, and respond to failures is neither efficient nor scalable.
Therefore, there is a pressing need for an intelligent, self-adaptive system that can
autonomously optimize network resources and swiftly address potential disruptions.
1.3 SIGNIFICANCE –
HOW SELF-ORGANIZATION CAN HELP 5G NETWORKS?
The idea of cognitive networking aims at bringing network automation to wireless
communication systems through introducing self-control to make the systems more
[7]
dynamic, agile, and efficient. In other words, since the primary goal is to minimize the
human intervention in communication networks, the response time to any changes and/or
faults in the systems is shortened, improving the dynamism and agility of the networks.
Moreover, provided that human interventions render the management of communication
systems more time-consuming and costly, reducing it would result in utilizing the
resources more efficiently. In addition, this is also beneficial for cellular network
operators, as their operational and capital expenditures are reduced, which in turn make
their businesses more sustainable and profitable.
Since self-protection could somehow be included in self-optimization and self-healing,
the focus of this work will be the first three characteristics, which are common between
cognitive networking.
Fig 1. How 5G and AI drive innovation in businesses
1.4 FIELD STUDY –
In conclusion, the convergence of 5G and AI technologies presents a wealth of
opportunities for businesses across various industries. By harnessing the power of 5G
connectivity and AI algorithms, businesses can unlock new levels of innovation,
efficiency, and customer satisfaction. Whether it’s hiring AI developers, partnering with
[8]
an AI development company, or availing AI development services, businesses can
leverage these technologies to drive growth and gain a competitive edge.
In sectors like gaming and entertainment, AI enhances user experiences through
personalized recommendations, immersive gameplay, and realistic virtual worlds. In
banking and fintech, AI enables advanced fraud detection, personalized financial services,
and real-time market insights. In healthcare, AI assists in diagnosis, treatment planning,
and remote patient monitoring, transforming the delivery of medical services. In
education, AI facilitates personalized learning, virtual tutoring, and collaborative remote
learning experiences. The possibilities are vast and span across industries.
Fig 2. Top use cases of 5G & AI
1.5 BRIEF DESCRIPTION OF THE SOLUTION APPROACH –
1.5.1 Although some form of learning and emergent intelligence is exhibited by natural SO
systems this intelligence may arise by following simple optimal rules at local entity
levels in the system rather than a complex learning process across the system as a
whole. Therefore, classic optimisation is an equally promising approach towards
designing SO in future cellular systems as long as it can yield solutions that are
scalable, stable and agile. Scalability and agility usually demand that the optimised
solution should be implementable in a distributed manner. Stability requires that it
should be optimal or at least feasible for all potential states of the system. i.e. The
optimisation process might have to consider multiple objectives simultaneously or be
[9]
bounded by feasibility constraints as optimisation of a single objective, while
neglecting others may lead to unstable or non pragmatic solutions. Further we discuss
both kinds of approaches.
1.5.2 To ensure high reliability of future networks, we have discussed the need to
continuously monitor the system for any anomaly, analysis and diagnosis if anomaly
is detected and trigger the right compensation action autonomously. Key advances in
cell outage detection and compensation schemes in literature were discussed.
However, the area of self healing has not gained much attention as compared to self
optimisation functionality in SON and a substantial amount of work is required before
full deployment of self healing functionality is a reality in future wireless cellular
systems.
[10]
CHAPTER 2 – LITERATURE SURVEY :
2.1 Title - "Self-Healing and Resilience in Future 5G Cognitive Autonomous Networks"
by Janne Ali-Tolppa, Szilard Kocsis, Benedek Schultz, Levente Bodrog, and Marton Kajo in
paragraph format:
The authors discuss methods for improving resiliency in future 5G mobile networks, with a
focus on self-healing capabilities within self-organizing network (SON) functions. They
propose a four-stage self-healing process encompassing profiling of normal network behavior,
detecting anomalies, diagnosing likely root causes, and taking automated corrective actions.
The anomaly detection method models normal behavior for various network metrics across
contexts like cells and days of the week in order to detect deviations. It creates profiles
capturing daily patterns and correlations between metrics. The diagnosis approach uses casebased reasoning to match anomaly patterns against a knowledgebase of previously labeled
cases to determine the most probable root cause. Additionally, active learning techniques are
employed to select the most informative anomaly cases for human experts to analyze and add
to the diagnosis knowledgebase. The authors further highlight the need for holistic monitoring
and coordination between SON functions across network domains and layers in order to get an
integrated view of anomalies and actions. This enables the prevention of later more catastrophic
failures since different SON functions can collaboratively monitor each other's corrective
actions. In conclusion, the self-healing and diagnosis methods proposed in this paper aim to
improve resilience against unforeseen network issues in future 5G networks. Sharing
knowledge and enabling coordination between SON functions is key for overall network
resilience.
[11]