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MODELING, ANALYSIS AND DESIGN OF MULTI-CHANNEL BONDING FOR IEEE 802.11 WLANS
Title
MODELING, ANALYSIS AND DESIGN OF MULTI-CHANNEL BONDING FOR IEEE 802.11 WLANS
Creator
Khairy, Sami
Date
2016, 2016-12
Description
The aim of this dissertation is to study the performance of distributed and opportunistic multi-channel bonding protocol in IEEE 802.11ac...
Show moreThe aim of this dissertation is to study the performance of distributed and opportunistic multi-channel bonding protocol in IEEE 802.11ac WLANs, and design channel bonding strategies to efficiently utilize the available spectrum. To this end, we first develop an analytical framework to study the throughput performance of WLANs with co-existing ac users and legacy users, characterizing the contentions among ac and legacy users in both primary and secondary channels. By modeling the transmissions of legacy users and ac users with and without bonding as a twolevel renewal process, the channel bonding probability of ac users in each secondary channel can be derived. Based on the bonding probability, MAC throughput of ac and legacy users can be analyzed respectively. Our analysis show that in a homogeneous multi-channel WLAN where only ac users are present, the contention probability of ac users is the same as that in a single channel with the same number of users; and in a heterogeneous WLAN with both ac and legacy users, an ac user can achieve a higher throughput than a legacy user, although the overall throughput decreases due to the increased contention level imposed by ac users in secondary channels. Based on the analysis, we further propose a channel selection strategy for ac users to select the best primary channel, in order to mitigate the contentions in the network and attain the maximal throughput. Analytical results show that primary channel selection is indifferent in a homogeneous network, whereas in a heterogeneous network, ac users should select the least congested channel as the primary channel to attain the maximal throughput. To evaluate the performance of a multi-channel WLAN, we develop an event-driven simulator based on network simulator-3 (NS-3). Extensive simulations validate our analyses and the efficiency of the proposed channel selection strategy.
M.S. in Electrical Engineering, December 2016
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DATA-DRIVEN OPTIMIZATION OF NEXT GENERATION HIGH-DENSITY WIRELESS NETWORKS
Title
DATA-DRIVEN OPTIMIZATION OF NEXT GENERATION HIGH-DENSITY WIRELESS NETWORKS
Creator
Khairy, Sami
Date
2021
Description
The Internet of Things (IoT) paradigm is poised to advance all aspects of modern society by enabling ubiquitous communications and...
Show moreThe Internet of Things (IoT) paradigm is poised to advance all aspects of modern society by enabling ubiquitous communications and computations. In the IoT era, an enormous number of devices will be connected wirelessly to the internet in order to enable advanced data-centric applications. The projected growth in the number of connected wireless devices poses new challenges to the design and optimization of future wireless networks. For a wireless network to support a massive number of devices, advanced physical layer and channel access techniques should be designed, and high-dimensional decision variables should be optimized to manage network resources. However, the increased network scale, complexity, and heterogeneity, render the network unamenable to traditional closed-form mathematical analysis and optimization, which makes future high-density wireless networks seem unmanageable. In this thesis, we study the design and data-driven optimization of future high-density wireless networks operating over the unlicensed band, including Radio Frequency (RF)-powered wireless networks, solar-powered Unmanned Aerial Vehicle (UAV)-based wireless networks, and random Non-Orthogonal Multiple Access (NOMA) wireless networks. For each networking scenario, we first analyze network dynamics and identify performance trade-offs. Next, we design adaptive network controllers in the form of high-dimensional multi-objective optimization problems which exploit the heterogeneity in users' wireless propagation channels and energy harvesting to maximize the network capacity, manage battery energy resources, and achieve good user capacity fairness. To solve the high-dimensional optimization problems and learn the optimal network control policy, we propose novel, cross-layer, scalable, model-based and model-free data-driven network optimization and resource management algorithms that integrate domain-specific analyses with advanced machine learning techniques from deep learning, reinforcement learning, and uncertainty quantification. Furthermore, convergence of the proposed algorithms to the optimal solution is theoretically analyzed using mathematical results from metric spaces, convex optimization, and game theory. Finally, extensive simulations have been conducted to demonstrate the efficacy and superiority of our network optimization and resource management techniques compared with existing methods. Our research contributions provide practical insights for the design and data-driven optimization of next generation high-density wireless networks.
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