Abstract

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.