Secure and Intelligent Resource Allocation for Low-Latency Wireless Communications

Abstract

Low-latency wireless communications have attracted intense attention due to emerging network applications such as online gaming, video conferencing, autonomous driving, remote surgery, and virtual reality. A key challenge for achieving low-latency communications is to design optimal resource allocation frameworks that can satisfy multiple network and user requirements since storage, computing, and bandwidth resources are severely limited in real-world wireless communications. An important consideration for resource allocation is to satisfy diverse network and user security requirements due to a wide range of attacks that can jeopardize data security and service-provisioning of low-latency wireless communications. Furthermore, resource allocation needs to be highly adaptable to the dynamics of users and wireless networks in diverse network scenarios, including the Internet of Things (IoT), mobile edge computing (MEC), and network slicing (NS). In this thesis, in light of above challenges, we propose novel resource allocation frameworks that can effectively reduce latency whilst resisting communications and data security attacks, as well as intelligently adapting to the dynamics of users in a range of emerging wireless network scenarios.