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
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)
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 mobility signaling congestion in LTE by overlapping tracking area lists
Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
Performance and cost trade-off in Tracking Area reconfiguration: A Pareto-optimization approach
Computer Networks: The International Journal of Computer and Telecommunications Networking
| Hi-index | 0.00 |
A key component in location management in Long Term Evolution (LTE) networks is the design of tracking areas (TAs). TA design must be revised over time in order to adapt to changes and trends in user location and mobility patterns. In this paper we present a re-optimization approach for revising a given TA design. The approach is justified by the fact that, once a TA design is in use, it is not feasible to deploy a greenfield design that significantly differs from the current one. By re-optimization, the design is successively improved by re-assigning some cells to TAs other than their current ones. Moreover, to account for the service interruption of TA reconfiguration, there is a limit on the amount of traffic affected by the re-assignments. To solve the resulting NP-hard optimization problem, we develop an algorithm based on repeated local search. We present computational experiments for a realistic TA planning scenario for the city of Lisbon. The experiments demonstrate the effectiveness of the proposed approach.