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Ultra-Dense Networks in 5G and Beyond: Challenges and Promising Solutions


Ultra-Dense Networks in 5G and Beyond: Challenges and Promising Solutions

Elbayoumi, Mohammed (2023) Ultra-Dense Networks in 5G and Beyond: Challenges and Promising Solutions. PhD thesis, Concordia University.

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Ultra-Dense Network (UDN) is one of the promising and leading directions in Fifth Generation and beyond (5GB) networks. In UDNs, Small Cells (SCs) or Small Base Stations (SBSs) such as microcells, picocells, or femtocells are deployed in high densities where inter-site distances are within the range of few or tens of meters. UDNs also require that SCs are typically deployed in relatively large densities compared to the Human-Type Communication Users (HTCUs) such as smartphones, tablets, and/or laptops. Such SCs are characterized by their low transmission powers, small coverage areas, and low cost. Hence, the deployment of the SCs can be done either by the cellular network operators or by the customers themselves within their premises to maintain certain levels of Quality of Service (QoS).
However, the randomness of the deployment of the SCs along with the small inter-site distances may degrade the achievable performance due to the uncontrolled Inter-Cell Interference (ICI). Therefore, idle mode capability is an inevitable feature in the high-density regime of SCs. In idle mode, a SC is switched off to prevent ICI when no user is associated to it. In doing so, we can imagine the UDN as a mobile network that keeps following the users to remain as close as possible to them.

In 5G, different use cases are required to be supported such as enhanced Mobile Broad-Band (eMBB), Ultra-Reliable and Low-Latency Communication (URLLC), and massive Machine-Type Communication (mMTC). On one hand, the inevitable upcoming era of smart living requires unprecedented advances in enabling technologies to support the main building blocks of this era which are Internet of Things (IoT) devices. Machine-Type Communication (MTC), the cellular version of Machine-to-Machine (M2M) communication, constitutes the main enabling technology to support communications among such devices with minimal or even without human intervention. The massive number of these devices, Machine-Type Communication Devices (MTCDs), and the immense amount of traffic generated by them require a paramount shift in cellular and non-cellular wireless technologies to achieve the required connectivity. On the other hand, the sky-rocketing number of data hungry applications installed on human-held devices, or HTCUs, such as video conferencing and virtual reality applications require their own advances in the wireless infrastructure in terms of high capacity, enhanced reliability, and reduced latency.

Throughout this thesis, we exploit the UDN infrastructure integrated with other 5G resources and enabling technologies to explore the possible opportunities in supporting both HTC and MTC, either solely or simultaneously. Given the shorter distances between transmitters and receivers encountered in UDNs, more realistic models of the path loss must be adopted such as the Stretched Exponential Path Loss (SEPL) model. We use tools from stochastic geometry to formulate novel mathematical frameworks that can be used to investigate the achievable performance without having to rely on extensive time-consuming Monte-Carlo simulations. Besides, the derived analytical expressions can be used to tune some system parameters or to propose some approaches/techniques that can be followed to optimize the performance of the system under certain circumstances.

Tackling practical scenarios, the complexity, or sometimes in-feasibility, of providing unlimited backhaul capacity for the massive number of SCs must be considered. In this regard, we adopt multiple-association where each HTCU is allowed to associate with multiple SCs. By doing so, we carefully split the targeted traffic among several backhaul links to mitigate the bottleneck forced by limited backhaul capacities. It is noteworthy that for coexisting MTCDs with the HTCUs, activating more SCs would allow more MTCDs to be supported without introducing additional ICI towards the HTCUs.

Targeting different application, multiple-association can be also adopted to tackle computation-intensive applications of HTCUs. In particular, for applications such as augmented reality and environment recognition that require heavy computations, a task is split and partially offloaded to multiple SCs with integrated Edge Computing Servers (ECSs). Then, the task partitions are processed in parallel to reduce the end-to-end processing delay. Based on relative densities between HTCUs and SCs, we use tools from stochastic geometry to develop an offline adaptive task division technique that further reduces the average end-to-end processing delay per user.

With the frequent serious data breaches experienced in recent years, securing data has become more of a business risk rather than an information technology (IT) issue. Hence, we exploit the dense number of SCs found in UDN along with Physical Layer Security (PLS) protocols to secure data transfer. In particular, we again adopt multiple-association and split the data of HTCUs into multiple streams originating from different SCs to prevent illegitimate receivers from eavesdropping.

To support massive number of MTCDs, we deploy the Non-Orthogonal Multiple-Access (NOMA) technique. Using power NOMA, more than one device can be supported over the same frequency/time resource and their signals are distinguished at the receiver using Successive Interference Cancellation (SIC). In the same scope, exploiting the available resources in 5G and beyond networks, we investigate a mMTC scenario in an UDN operating in the Millimeter Wave (mmWave) band and supported by wireless backhauling. In doing so, we shed lights on the possible gains of utilizing the mmWave band where the severe penetration losses of mmWave can be exploited to mitigate the significant ICI in UDNs. Also, the vast bandwidth available in the mmWave band helps to allocate more Resource Blocks (RBs) per SCs which corresponds to supporting more MTCDs.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Thesis (PhD)
Authors:Elbayoumi, Mohammed
Institution:Concordia University
Degree Name:Ph. D.
Program:Information and Systems Engineering
Date:16 January 2023
Thesis Supervisor(s):Youssef, Amr and Hamouda, Walaa
ID Code:991862
Deposited By: Mohammed Mohammed Elbayoumi Elsayed
Deposited On:21 Jun 2023 14:41
Last Modified:21 Jun 2023 14:41
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