Movement-based location update and selective paging for PCS networks
IEEE/ACM Transactions on Networking (TON)
Minimizing the average cost of paging under delay constraints
Wireless Networks
A selective location update strategy for PCS users
Wireless Networks
Location area planning for personal communication services networks
MSWiM '99 Proceedings of the 2nd ACM international workshop on Modeling, analysis and simulation of wireless and mobile systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Optimal Location Area Design to Minimize Registration Signaling Traffic in Wireless Systems
IEEE Transactions on Mobile Computing
Efficient location area planning for personal communication systems
IEEE/ACM Transactions on Networking (TON)
Performance improvement of LTE tracking area design: a re-optimization approach
Proceedings of the 6th ACM international symposium on Mobility management and wireless access
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
Location area planning and cell-to-switch assignment in cellular networks
IEEE Transactions on Wireless Communications
A profile-based location strategy and its performance
IEEE Journal on Selected Areas in Communications
Mitigating signaling congestion in LTE location management by overlapping tracking area lists
Computer Communications
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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 reconfiguration cost. 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.