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Learning and Reasoning Strategies for User Association in Ultra-dense Small Cell Vehicular Networks

Kapoor, S (2019) Learning and Reasoning Strategies for User Association in Ultra-dense Small Cell Vehicular Networks. PhD thesis, University of York.

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Thesis_Learning and Reasoning Strategies for User Association in Ultra-dense Small Cell Vehicular Networks_ShipraKapoor.pdf - Examined Thesis (PDF)
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Abstract

Recent vehicular ad hoc networks research has been focusing on providing intelligent transportation services by employing information and communication technologies on road transport. It has been understood that advanced demands such as reliable connectivity, high user throughput, and ultra-low latency required by these services cannot be met using traditional communication technologies. Consequently, this thesis reports on the application of artificial intelligence to user association as a technology enabler in ultra-dense small cell vehicular networks. In particular, the work focuses on mitigating mobility-related concerns and networking issues at different mobility levels by employing diverse heuristic as well as reinforcement learning (RL) methods. Firstly, driven by rapid fluctuations in the network topology and the radio environment, a conventional, three-step sequence user association policy is designed to highlight and explore the impact of vehicle speed and different performance indicators on network quality of service (QoS) and user experience. Secondly, inspired by control-theoretic models and dynamic programming, a real-time controlled feedback user association approach is proposed. The algorithm adapts to the changing vehicular environment by employing derived network performance information as a heuristic, resulting in improved network performance. Thirdly, a sequence of novel RL based user association algorithms are developed that employ variable learning rate, variable rewards function and adaptation of the control feedback framework to improve the initial and steady-state learning performance. Furthermore, to accelerate the learning process and enhance the adaptability and robustness of the developed RL algorithms, heuristically accelerated RL and case-based transfer learning methods are employed. A comprehensive, two-tier, event-based, system level simulator which is an integration of a dynamic vehicular network, a highway, and an ultra-dense small cell network is developed. The model has enabled the analysis of user mobility effects on the network performance across different mobility levels as well as served as a firm foundation for the evaluation of the empirical properties of the investigated approaches.

Item Type: Thesis (PhD)
Academic Units: The University of York > Electronics (York)
Depositing User: Ms. S Kapoor
Date Deposited: 18 Mar 2019 11:24
Last Modified: 18 Mar 2019 11:24
URI: http://etheses.whiterose.ac.uk/id/eprint/23261

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