Knowledge-Defined Routing Strategies for Next Generation Wireless Networks

Ashtari Nakhaei, Sepehr

Knowledge-Defined Routing Strategies for Next Generation Wireless Networks

Ashtari Nakhaei, Sepehr

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Publication Type:: Thesis
Issue Date:: 2023

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Field	Value	Language
dc.contributor.author	Ashtari Nakhaei, Sepehr
dc.date.accessioned	2023-09-27T00:43:19Z
dc.date.available	2023-09-27T00:43:19Z
dc.date.issued	2023
dc.identifier.uri	http://hdl.handle.net/10453/172304
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	The proliferation of smart devices and corresponding applications has resulted in a significant increase in cellular network traffic. To mitigate this, device-to-device (D2D) communication has been proposed as an efficient solution for reducing network congestion and increasing network capacity. D2D was first introduced by the Third Generation Partnership Project (3GPP) as a key enabling technology for the fifth-generation (5G) of cellular networks. However, the limitations of D2D have led to the emergence of multi-hop D2D (MD2D) as a potential replacement and an enabling technology for future generations of cellular networks, such as the sixth generation (6G) and beyond. The first objective of this thesis is to study the new network architecture known as knowledge-defined networking (KDN) and the benefits of various MD2D routing protocols and frameworks. We conducted a comprehensive literature review that revealed two key findings. First, future network architecture should integrate automated techniques to enhance network performance. Secondly, centralized controllers and virtualization technologies, such as software-defined networking (SDN) and network function virtualization (NFV), are crucial for achieving high performance in MD2D networks. As a result, we targeted to study centralized and automated MD2D routing frameworks and protocols. The second objective of this thesis was to enhance the energy efficiency of MD2D routing protocols by introducing efficient and intelligent routing methods. We proposed two intelligent routing protocols that leverage network knowledge to construct routing tables. The first method employs a centralized participation mechanism that utilizes a fuzzy logic system to identify eligible nodes for MD2D communication based on multiple network metrics. The second method uses various utility functions to create an automated routing mechanism to alternate paths, thus increasing network lifetime and throughput. The final objective of this thesis is to enhance MD2D performance with regard to traffic offloading and end-to-end (E2E) delay. To achieve this, we employed virtualization techniques in conjunction with WiFi network slicing to segment various nodes into distinct network slices to optimize throughput and E2E delay. Additionally, we introduced a location-based routing strategy to augment cellular traffic offloading. This MD2D routing protocol utilizes the location of nodes in conjunction with network topology to determine efficient and dependable routes, thereby reducing E2E delay and increasing throughput, ultimately enabling offloading of a greater number of packets from the BS. In conclusion, this thesis presents an in-depth examination of MD2D networks and proposes intelligent and high-performance MD2D routing protocols and frameworks.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/172304/1/thesis.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© 2023 Sepehr Ashtari Nakhaei
dc.rights	au.edu.uts.lib/cph
dc.title	Knowledge-Defined Routing Strategies for Next Generation Wireless Networks	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

The proliferation of smart devices and corresponding applications has resulted in a significant increase in cellular network traffic. To mitigate this, device-to-device (D2D) communication has been proposed as an efficient solution for reducing network congestion and increasing network capacity. D2D was first introduced by the Third Generation Partnership Project (3GPP) as a key enabling technology for the fifth-generation (5G) of cellular networks. However, the limitations of D2D have led to the emergence of multi-hop D2D (MD2D) as a potential replacement and an enabling technology for future generations of cellular networks, such as the sixth generation (6G) and beyond. The first objective of this thesis is to study the new network architecture known as knowledge-defined networking (KDN) and the benefits of various MD2D routing protocols and frameworks. We conducted a comprehensive literature review that revealed two key findings. First, future network architecture should integrate automated techniques to enhance network performance. Secondly, centralized controllers and virtualization technologies, such as software-defined networking (SDN) and network function virtualization (NFV), are crucial for achieving high performance in MD2D networks. As a result, we targeted to study centralized and automated MD2D routing frameworks and protocols. The second objective of this thesis was to enhance the energy efficiency of MD2D routing protocols by introducing efficient and intelligent routing methods. We proposed two intelligent routing protocols that leverage network knowledge to construct routing tables. The first method employs a centralized participation mechanism that utilizes a fuzzy logic system to identify eligible nodes for MD2D communication based on multiple network metrics. The second method uses various utility functions to create an automated routing mechanism to alternate paths, thus increasing network lifetime and throughput. The final objective of this thesis is to enhance MD2D performance with regard to traffic offloading and end-to-end (E2E) delay. To achieve this, we employed virtualization techniques in conjunction with WiFi network slicing to segment various nodes into distinct network slices to optimize throughput and E2E delay. Additionally, we introduced a location-based routing strategy to augment cellular traffic offloading. This MD2D routing protocol utilizes the location of nodes in conjunction with network topology to determine efficient and dependable routes, thereby reducing E2E delay and increasing throughput, ultimately enabling offloading of a greater number of packets from the BS. In conclusion, this thesis presents an in-depth examination of MD2D networks and proposes intelligent and high-performance MD2D routing protocols and frameworks.

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