Along with the evolution of network technologies, user’s expectations on performance and mobile services are rising rapidly. To fulfill the customer demands the operators are facing a great amount of network signaling overhead in terms of Location Management (LM), which tracks and pages User Equipments (UEs) in the network. Hence, sustaining a reliable and cost-efficient LM system for future mobile broadband networks has become one of the major challenges in mobile telecommunications. Tracking Area (TA) in Long Term Evolution (LTE), is a logical grouping of cells, that manages and represents the locations of UEs. This dissertation deals with planning and optimization of TAs.

TA design must be revised over time in order to adapt to changes and trends in UE location and mobility patterns. Re-optimization of a once-optimized design subject to different cost budgets is one of the problems considered in this dissertation. By re-optimization, the design is successively improved by re-assigning some cells to TAs other than their original ones. For solving the resulting problem, an algorithm based on repeated local search is developed.

The next topic of research is the trade-off between the performance in terms of the total signaling overhead of the network and the reconfiguration cost. This trade-off is modeled as a bi-objective optimization problem to which the solutions are characterized by Pareto-optimality. Solving the problem delivers a host of potential trade-offs among which the selection can be based on the preferences of a decision-maker. An integer programming model and a genetic algorithm heuristic are developed for solving the problem in large-scale networks.

In comparison to previous generations of cellular networks, LTE systems allow for a more flexible configuration of TA design by means of Tracking Area List (TAL). How to utilize this flexibility in applying TAL to large-scale networks is still an open problem. In this dissertation, three approaches for allocating and assigning TALs are presented, and their performances are compared with each other, as well as with the conventional TA scheme. Moreover, a linear programming model is developed to minimize the total signaling overhead of the network based on overlapping TALs.

In this dissertation, the problem of mitigating signaling congestion is thoroughly studied both for the specific train scenario and also for the general network topology. For each signaling congestion scenario, a related linear programming model based on minimizing the maximum signaling due to tracking area update or paging is developed. As a major advantage of the modified overlapping TAL scheme for signaling congestion avoidance, information of individual UE mobility is not required.

Automatic reconfiguration of LM is an important element in LTE. The network continuously collects UE statistics, and the management system adapts the network configuration to changes in UE distribution and demand. In this dissertation an evaluation of dynamic configuration of TA design, including the use of overlapping TAL for congestion mitigation, is performed and compared to the static configuration by using a case study.

The enormous competition in the telecommunications market results in the necessity of optimized and cost-efficient networks for the operators and service providers. Tracing users cost-efficiently is one of the major challenges in the study of location management of wireless cellular  networks. Tracking Area (TA) is a logical grouping of cells in Long Term Evolution (LTE) networks. TA manages and represents the location of User Equipments (UEs). One of the well-known performance consideration is the signaling overhead of tracking area update versus that for paging. This thesis deals with planning and optimization of tracking area configuration in LTE networks.

TA design must be revised over time in order to adapt to changes and trends in UE location and mobility patterns. Re-optimization of the initial planning subject to different cost budgets is one of the problems considered in the thesis. By re-optimization, the design is successively improved by re-assigning some cells to TAs other than their original ones. To solve the resulting problem, an algorithm based on repeated local search is developed.

By extending the line of research, the trade-off between the performance in terms of overall signaling overhead of the network and the reconfiguration cost is considered. This trade-off is modeled as a biobjective optimization problem to which the solutions are characterized by pareto-optimality. Solving the problem delivers a host of potential trade-offs among which the selection can be based on the preferences of a decision-maker. An integer programming model and a heuristic based on genetic algorithm are developed for solving the problem in large-scale networks.

In comparison to earlier generations of cellular networks, LTE systems allow for a more flexible configuration of TA design by means of Tracking Area List (TAL). How to utilize this flexibility in applying TAL to large-scale networks remains unexplored. In this thesis, three approaches for allocating and assigning TA lists have been presented, and their performance is compared with each other, as well as with the standard location management scheme.

Automatic reconfiguration is an important element in LTE. The network continuously collects UE statistics, and the management system adapts the network configuration to changes in UE distribution and demand. In this thesis an evaluation of dynamic configuration of TA design, including the use of TAL, has been performed and compared to the static configuration by using a case study.

Avoiding signaling congestion is a key aspect in location management of cellular networks. The signaling load in this context corresponds to the use of location update and paging messages for tacking user equipments (UEs). Signaling congestion may occur due to many UEs behaving in a similar manner, e.g., massive and simultaneous UE mobility in a train movement scenario. For Long Term Evolution (LTE) networks, location management can be based on the use of tracking area lists (TALs), each being a list containing multiple tracking areas (TAs). In this paper, the scheme of using TALs with overlapping TAs is applied for signaling congestion mitigation. One or multiple TALs that potentially overlap are allocated to each cell, and the TALs are assigned to the UEs that make their location updates in the cell. We derive linear programming formulations for finding the optimal proportional use of overlapping TALs for congestion avoidance. The main advantage of our approach is that it does not require the collection of detailed UE mobility information apart from what is available to the network. We report numerical results for realistic scenarios. The optimization approach yields promising results for reducing signaling congestion, and the performance region of optimized overlapping TALs goes significantly beyond the capability of the conventional TA scheme.

Improving the tracking areas (TA) configuration over time to reduce signaling overhead is vital for location management of Long Term Evolution networks. The user location and mobility patterns have the tendency to change over time, and consequently the TA layout needs to be revised. A TA reconfiguration would cause service interruption and "cost". Hence there will always be a tradeoff between the total signaling overhead and the reconfiguration cost. In this paper a genetic algorithm together with local search are applied to this bi-objective problem. Unlike previous methods, our approach does not need to weight together the two objective functions. Instead, the algorithm is designed to deliver various pareto-optimal solutions in a single run. Computational experiments are presented for a realistic TA planning scenario of Lisbon city. The results illustrate the ability of the approach to address the primary concern in decision making of an operator - benefit versus cost, by means of providing multiple reconfiguration choices with various levels of trade-off between the two factors.

Reducing the total signaling overhead of tracking and paging user equipments (UEs) is one of the most challenging problems in the study of location management of cellular networks. Since the introduction of the list concept by 3GPP standards for Long Term Evolution (LTE) networks, defeating some of the existing limitations of the conventional Tracking Area (TA) design has been expected. However, how to exploit this flexibility in the system, without the existence of UEs' mobility traces, is still an open problem. In this paper, first we modify the linear program (LP) formulation of the overlapping tracking area list (TAL) scheme, which previously was only used for mitigating signaling congestion, to fit the objective of minimizing the total signaling overhead of tracking area update (TAU) and paging in a network. Second, we analyze the solution characteristics of the LP model. Finally, we perform a justified evaluation of the overlapping TAL scheme and the conventional TA scheme on a large-scale network by using two methods for calculating the signaling overhead of the aggregated data. Our modified LP model configures overlapping TALs for each cell in the network with the purpose of minimizing the total signaling overhead of location management in LTE networks. Analytical results demonstrate that although the optimality of the TAL design is valid for a limited period of time, by overcoming the limitations of symmetric and transitive properties of the conventional TA scheme, the proposed overlapping TAL scheme is able to reduce the total signaling overhead compared to what ultimately is achievable by the conventional TA design.

Reducing the signaling overhead for tracing user equipment (UE), while maintaining the improved performance over time despite the changes in UE location and mobility patterns, is a challenging issue in the area of mobility management. Flexibility and automatic reconfiguration are two significant features in Long Term Evolution (LTE) systems. The Tracking Area List (TAL) is a novel concept in LTE systems, which allows a more flexible configurations, expecting to reduce the overall signaling overhead. In this paper, we first present a ”rule of thumb” method to allocate and assign TALs for a network. The easily applied approach does not require any data other than what is available for conventional TA design. Second we compare the performance of an optimum conventional TA design with the suggested TAL design for a large scale network in Lisbon, Portugal. A thorough computation is done to make a justified evaluation. We follow the comparison during specific time intervals for one complete day, and we illustrate the performance of reconfiguration for each approach. The results clearly demonstrate the ability of dynamic TAL in reducing the signaling overhead and maintaining a good performance due to reconfiguration compared to the conventional TA design.

Reducing the overhead required for tracing mobile devices is one of the major aspects in the study of mobility management of a cellular network. The Long Term Evolution (LTE) systems give a more flexible configuration of Tracking Area (TA) design by means of Tracking Area List (TAL). Being a novel concept, TAL goes beyond the capability of the conventional TA approach. Although TAL is expected to be able to reduce the overall signaling overhead by overcoming a couple of major limitations of the conventional TA concept, how to apply TAL in large scale networks, remains unexplored. In this paper, we present a novel approach for allocating and assigning TA lists. The approach does not require any data other than what is needed for conventional TA design. We present numerical results to illustrate the approach for a realistic network of Lisbon city. The experiments demonstrate the ability of TAL in reducing the signaling overhead compared to the conventional TA concept.

Mitigating signaling congestion of tracing user equipments (UEs), adaptively to the changes in UE location and mobility patterns is a challenging issue in mobility management of Long Term Evolution (LTE) networks. Signaling congestion usually occurs due to many UEs behaving in a similar manner, e.g., massive and simultaneous UE mobility in a train movement scenario. LTE networks allow the use of tracking area lists (TALs), each being a list containing multiple tracking areas (TAs). The overlapping TAL scheme has been previously used for signaling congestion mitigation for snapshot scenarios. For maintaining the improved performance over non-list-oriented TA configuration over time, an automatic dynamic configuration framework, which is a key aspect in Self-Organizing Network (SON), has been applied in this paper. We develop a linear programming model for optimal TAL configuration. Repetitively solving the model for different time intervals gives an evaluation framework on the performance of SON location management. Comprehensive numerical results are presented in this study using a large-scale realistic network scenario. The experiments demonstrate the effectiveness of the SON dynamic framework in reducing the total signaling overhead of the network compared to the static TA. Moreover, the overlapping TAL scheme significantly improves the performance of the network in the tracking area update congested scenarios over the conventional TA configuration.

Tracking Area (TA) design is one of the key tasks in location management of Long Term Evolution (LTE) networks. TA enables to trace and page User Equipments (UEs). As UEs distribution and mobility patterns change over time, TA design may have to undergo revisions. For revising the TA design, the cells to be reconfigured typically have to be temporary torn down. Consequently, this will result in service interruption and “cost”. There is always a trade-off between the performance in terms of the overall signaling overhead of the network and the reconfigurationcost. In this paper, we model this trade-off as a bi-objective optimization problem to which the solutions are characterized by Pareto-optimality. Solving the problem delivers a host of potential trade-offs among which the selection can be based on the preferences of a decision maker. An integer programming model has been developed and applied to the problem. Solving the integer programming model for various cost budget levels leads to an exact scheme for Pareto-optimization. In order to deliver Pareto-optimal solutions for large networks in one single run, a Genetic Algorithm (GA) embedded with Local Search (LS) is applied. Unlike many commonly adopted approaches in multi-objective optimization, our algorithm does not consider any weighted combination of the objectives. Comprehensive numerical results are presented in this study, using large-scale realistic or real-life network scenarios. The experiments demonstrate the effectiveness of the proposed approach.

Optical Voltage Transducer (OVT) is considered as a proper candidate for replacing the conventional inductive and capacitive transformers. The exact positioning of the electric field sensors are one of the important issues while designing an OVT. The three sensors of the OVT have been positioned according to quadrature method to satisfy the IEC 0.2% class specification. A three part OVT is designed and numerically analyzed by FLUX software and the results for different external perturbations are maintaining the 0.2% class accuracies for the given system. The advantages of using a three part OVT have been considered in this paper.