Optimizing Resource Allocation with Energy Efficiency and Backhaul Challenges

To meet the requirements of future wireless mobile communication which aims to increase the data rates, coverage and reliability while reducing energy consumption and latency, and also deal with the explosive mobile traffic growth which imposes high demands on backhaul for massive content delivery, developing green communication and reducing the backhaul requirements have become two significant trends. One of the promising techniques to provide green communication is wireless power transfer (WPT) which facilitates energy-efficient architectures, e.g. simultaneous wireless information and power transfer (SWIPT). Edge caching, on the other side, brings content closer to the users by storing popular content in caches installed at the network edge to reduce peak-time traffic, backhaul cost and latency. In this thesis, we focus on the resource allocation technology for emerging network architectures, i.e. the SWIPT-enabled multiple-antenna systems and cache-enabled cellular systems, to tackle the challenges of limited resources such as insufficient energy supply and backhaul capacity. We start with the joint design of beamforming and power transfer ratios for SWIPT in MISO broadcast channels and MIMO relay systems, respectively, aiming for maximizing the energy efficiency subject to both the Quality of Service (QoS) constraints and energy harvesting constraints. Then move to the content placement optimization for cache-enabled heterogeneous small cell networks so as to minimize the backhaul requirements. In particular, we enable multicast content delivery and cooperative content sharing utilizing maximum distance separable (MDS) codes to provide further caching gains. Both analysis and simulation results are provided throughout the thesis to demonstrate the benefits of the proposed algorithms over the state-of-the-art methods

Integrated and Heterogenous Mobile Edge Caching (MEC) Networks

The recent phenomenal growth of the global mobile data traffic, mainly caused by intelligent Internet of Things (IoTs), is the most significant challenge of wireless networks within the foreseeable future. In this context, Mobile Edge Caching (MEC) has been recognized as a promising solution to maintain low latency communication. This, in turn, improves the Quality of Service (QoS) by storing the most popular multimedia content close to the end-users. Despite extensive progress in MEC networks, however, there are still limitations that should be addressed. Through this Ph.D. thesis, first, we perform a literature review on recent works on MEC networks to identify challenges and potential opportunities for improvement. Then, by highlighting potential drawbacks of the reviewed works, we aim to not only enhance the cache-hit-ratio, which is the metric to quantify the users’ QoS, but also to improve the quality of experience of caching nodes. In this regard, we design and implement a Deep Reinforcement Learning (DRL)-based connection scheduling framework [1] to minimize users’ access delay by maintaining a trade-off between the energy consumption of Unmanned Aerial Vehicles (UAVs) and the occurrence of handovers. We also use D2D communication [2] to increase the network’s capacity without adding any infrastructure. Another approach to effectively use the limited storage capacity of caching nodes is to increase the content diversity by employing the coded caching strategies in cluster-centric networks. Despite all the researches on the cluster-centric cellular networks, there is no framework to determine how different segments can be cached to increase the data availability in a UAV-aided cluster-centric cellular network. Moreover, to date, limited research has been performed on UAV-aided cellular networks to provide high QoS for users in both indoor and outdoor environments. Through this thesis research, we aim to address these gaps [3,4]. In addition, another goal of this thesis is to design real-time caching strategies [5–9] to predict the upcoming most popular content to improve the users’ access delay. Last but not least, capitalizing on recent advancements of indoor localization frameworks [10–14], we aim to develop a proactive caching strategy for an integrated indoor/outdoor MEC network