Optimization and deep learning techniques for next-generation wireless communication networks
AffiliationElectrical and Electronic Engineering
Document TypePhD thesis
Access StatusOpen Access
© 2019 Xiangyue Meng
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.
Keywords5G wireless networks; Backhaul; Full duplex; Cognitive radios; Spectrum sensing; Machine learning; Deep reinforcement learning; Internet of Things; Self-organized networks; Monte Carlo tree search
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