Resource allocation and computation offloading in cloud-assisted wireless networks
Creators: name: Younis, Ayman Nasih Salman
Publisher: name: No Publisher Supplied
Year: 2022

description: The next radio generations of mobile networks, Fifth Generation (5G) and beyond, are expected to speed up the transition from monolithic and inflexible networks to agile and distributed networking elements that rely on "virtualization", "softwarization", openness, intelligent and yet fully interoperable Radio Access Network (RAN) components. At the same time, the increasing popularity of computation-intensive applications on mobile devices has contributed to the overwhelming mobile traffic volume that is pushing against the boundary of the current communication networks' capacity. Besides, mobile platforms are becoming the predominant medium of access to Internet services due to a tremendous increase in their computation and communication capabilities. In light of this, cloud-assisted wireless networks are promising solutions that unite wireless networks and cloud computing to deliver cloud services directly from the network edges. The three emerging paradigms for cloud-assisted wireless networks are: Cloud Radio Access Network (C-RAN), in which the baseband resources are pooled at a Base Band Unit (BBU) based on the fundamentals of centralization and virtualization; Mobile-Edge Computing (MEC), which provides cloud computing and storage capabilities to enable rich services and applications in close proximity to the end-users; and Next Generation RAN (NG-RAN), in which the functional splitting technique is utilized to flexibly balance the radio and computation processes at Central Units (CUs) and Distributed Units (DUs). These paradigms are complementary and have unique justifications within the 5G and beyond ecosystem. The centralized nature of C-RAN provides a higher degree of cooperation in the network to address the capacity fluctuation and to increase the spectral and energy efficiency, whereas the MEC paradigm is useful in reducing service latency and improving localized user experience; on the other hand, NG-RAN provides flexible distribution of computation and radio capabilities at Base Stations (BSs). The goal of this research is to leverage the emerging C-RAN, MEC, and NG-RAN paradigms and to design disruptive innovations for wireless access networks in such a way as to always make the best use of the resources available so as to satisfy the service requests from the end-users. To this end, novel resource-allocation schemes and computation-offloading policies are proposed in this thesis to minimize the service latency and to improve the users' Quality of Experience (QoE). The proposed innovative solutions include: (I) a novel resource-allocation solution that aims at optimizing the energy consumption of a C-RAN, (ii) a novel resource-allocation scheme that aims at maximizing the network energy efficiency of a C-RAN subject to practical constraints, including Quality of Service (QoS) requirement, transmission power, and fronthaul capacity, (iii) a dynamic Video-streaming QoE Maximization (VQM) that takes into account the video Distortion Rate (DR) and the coordination among MEC server in order to enhance Adaptive Bitrate (ABR)-video streaming in a MEC network, (iv) a joint task offloading, latency, and Quality Loss of Result (QLR) framework, which helps improve users' computation experience by offloading their computation tasks to the edge servers, and (v) a novel Deep Reinforcement Learning-based Resource Allocation (ReLAX) framework to deal with the joint optimization of end-user association and power allocation in NG-RAN systems. The proposed innovations in this research can benefit a wide range of mobile applications and services such as video streaming, augmented reality (AR)/virtual reality (VR), Internet-of-Things (IoTs), and public safety operations.