Solving the Location Area Problem by Using Differential Evolution

In mobile networks, one of the hard tasks is to determine the best partitioning in the Location Area problem, but it is also an important strategy to try to reduce all the involved management costs. In this paper we present a new approach to solve the location management problem based on the Location Area partitioning, as a cost optimization problem. We use a Differential Evolution based algorithm to find the best configuration to the Location Areas in a mobile network. We try to find the best values for the Differential Evolution parameters as well as define the scheme that enables us to obtain better results, when compared to classical strategies and to other authors’ results. To obtain the best solution we develop four distinct experiments, each one applied to one Differential Evolution parameter. This is a new approach to this problem that has given us good results

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