User-oriented mobility management in cellular wireless networks

2020 Spring.Includes bibliographical references.Mobility Management (MM) in wireless mobile networks is a vital process to keep an individual User Equipment (UE) connected while moving within the network coverage area—this is required to keep the network informed about the UE's mobility (i.e., location changes). The network must identify the exact serving cell of a specific UE for the purpose of data-packet delivery. The two MM procedures that are necessary to localize a specific UE and deliver data packets to that UE are known as Tracking Area Update (TAU) and Paging, which are burdensome not only to the network resources but also UE's battery—the UE and network always initiate the TAU and Paging, respectively. These two procedures are used in current Long Term Evolution (LTE) and its next generation (5G) networks despite the drawback that it consumes bandwidth and energy. Because of potentially very high-volume traffic and increasing density of high-mobility UEs, the TAU/Paging procedure incurs significant costs in terms of the signaling overhead and the power consumption in the battery-limited UE. This problem will become even worse in 5G, which is expected to accommodate exceptional services, such as supporting mission-critical systems (close-to-zero latency) and extending battery lifetime (10 times longer). This dissertation examines and discusses a variety of solution schemes for both the TAU and Paging, emphasizing a new key design to accommodate 5G use cases. However, ongoing efforts are still developing new schemes to provide seamless connections to the ever-increasing density of high-mobility UEs. In this context and toward achieving 5G use cases, we propose a novel solution to solve the MM issues, named gNB-based UE Mobility Tracking (gNB-based UeMT). This solution has four features aligned with achieving 5G goals. First, the mobile UE will no longer trigger the TAU to report their location changes, giving much more power savings with no signaling overhead. Instead, second, the network elements, gNBs, take over the responsibility of Tracking and Locating these UE, giving always-known UE locations. Third, our Paging procedure is markedly improved over the conventional one, providing very fast UE reachability with no Paging messages being sent simultaneously. Fourth, our solution guarantees lightweight signaling overhead with very low Paging delay; our simulation studies show that it achieves about 92% reduction in the corresponding signaling overhead. To realize these four features, this solution adds no implementation complexity. Instead, it exploits the already existing LTE/5G communication protocols, functions, and measurement reports. Our gNB-based UeMT solution by design has the potential to deal with mission-critical applications. In this context, we introduce a new approach for mission-critical and public-safety communications. Our approach aims at emergency situations (e.g., natural disasters) in which the mobile wireless network becomes dysfunctional, partially or completely. Specifically, this approach is intended to provide swift network recovery for Search-and-Rescue Operations (SAROs) to search for survivors after large-scale disasters, which we call UE-based SAROs. These SAROs are based on the fact that increasingly almost everyone carries wireless mobile devices (UEs), which serve as human-based wireless sensors on the ground. Our UE-based SAROs are aimed at accounting for limited UE battery power while providing critical information to first responders, as follows: 1) generate immediate crisis maps for the disaster-impacted areas, 2) provide vital information about where the majority of survivors are clustered/crowded, and 3) prioritize the impacted areas to identify regions that urgently need communication coverage. UE-based SAROs offer first responders a vital tool to prioritize and manage SAROs efficiently and effectively in a timely manner

Intelligent Advancements in Location Management and C-RAN Power-Aware Resource Allocation

The evolving of cellular networks within the last decade continues to focus on delivering a robust and reliable means to cope with the increasing number of users and demanded capacity. Recent advancements of cellular networks such as Long-Term Evolution (LTE) and LTE-advanced offer a remarkable high bandwidth connectivity delivered to the users. Signalling overhead is one of the vital issues that impact the cellular behavior. Causing a significant load in the core network hence effecting the cellular network reliability. Moreover, the signaling overhead decreases the Quality of Experience (QoE) of users. The first topic of the thesis attempts to reduce the signaling overhead by developing intelligent location management techniques that minimize paging and Tracking Area Update (TAU) signals. Consequently, the corresponding optimization problems are formulated. Furthermore, several techniques and heuristic algorithms are implemented to solve the formulated problems. Additionally, network scalability has become a challenging aspect that has been hindered by the current network architecture. As a result, Cloud Radio Access Networks (C-RANs) have been introduced as a new trend in wireless technologies to address this challenge. C-RAN architecture consists of: Remote Radio Head (RRH), Baseband Unit (BBU), and the optical network connecting them. However, RRH-to-BBU resource allocation can cause a significant downgrade in efficiency, particularly the allocation of the computational resources in the BBU pool to densely deployed small cells. This causes a vast increase in the power consumption and wasteful resources. Therefore, the second topic of the thesis discusses C-RAN infrastructure, particularly where a pool of BBUs are gathered to process the computational resources. We argue that there is a need of optimizing the processing capacity in order to minimize the power consumption and increase the overall system efficiency. Consequently, the optimal allocation of computational resources between the RRHs and BBUs is modeled. Furthermore, in order to get an optimal RRH-to-BBU allocation, it is essential to have an optimal physical resource allocation for users to determine the required computational resources. For this purpose, an optimization problem that models the assignment of resources at these two levels (from physical resources to users and from RRHs to BBUs) is formulated