Electrical and Electronic Engineering - Theses
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ItemFinite-time algorithms and performance bounds for real-time Internet of Things( 2019)Rapid developments in technology have enabled a large scale deployment of interconnected sensors and actuators, captured under the umbrella term Internet of Things (IoT). Real-time IoT applications in smart grid, smart traffic control etc. are made possible by the real-time processing of high volume data, generated from dedicated and multi-purpose sensors, and exchanged over heterogeneous wired or wireless communication networks. Our work focuses explicitly on smart intersection management applications and develops and analyses algorithms to cater to its stringent latency, mobility and geo-distribution requirements. Considering the communication delays, distributed IoT implementations like these prefer fog/hybrid architecture-based data processing to the conventional centralised and cloud-based approach. Further, for relevant distributed real-time IoT algorithms, finite-time performance matters more than the asymptotic results in the literature . Thus, precise estimates have to be obtained on the delay needed for an optimisation algorithm to compute a solution within the desired proximity of the optimal solution. Such trade-offs are inevitable for the design of real-time algorithms over an IoT network. This thesis develops distributed optimisation algorithms, which involve explicit delay-accuracy trade-offs possible and studies the effects of channel impairments and communication network structure on them. We introduce a finite-time distributed optimisation algorithm and derives universal performance bounds for an asymptotic algorithm solving a quadratic Network Utility Maximisation (NUM) problem using quantised inter-agent communication. The finite-time algorithm is then used to solve a Model Predictive Control (MPC) problem and applied to a smart traffic intersection management scenario.
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ItemOptimization and deep learning techniques for next-generation wireless communication networks( 2019)Due to the explosive growth of consumer electronic devices, such as smartphones, tablets, and the Internet of Things, the global mobile data traffic is estimated to increase seven fold by 2022 in the fifth generation (5G) of wireless communication networks. At the same time, the mobile network connection speed is envisioned to increase more than three-fold by 2022. Many technologies have been proposed to fulfill such unprecedented user demands. On the macroscopic level, the network architecture largely determines the performance of the network. Novel network architectures, such as heterogeneous networks (HetNets) and centralized radio access networks (C-RANs), have been proposed to accommodate massive numbers of wireless devices. On the microscopic level, the control of all devices is vital for network operations on a daily basis. Intelligent control agents are hence required to operate networks without human intervention. In this thesis, we start off by focusing on the architecture side of network and investigate a MINLP problem of a joint backhual-access HetNet by using a classical optimization approach. Then, we move on to the operational side of networks and focus on spectrum sharing in cognitive radio networks and topology control in wireless sensor networks. In these problems, we employ deep learning approaches that can learn from collected data and adapt to the changing radio environment without a priori knowledge about the network. We show the applicability and superiority of our deep learning-based algorithms compared with classical analytic approaches. More importantly, we show the novel applicability of deep learning in solving MINLP problems that are commonly encountered engineering problems in wireless communication networks.