CDMA uplink power control as a noncooperative game
Wireless Networks
A utility-based power-control scheme in wireless cellular systems
IEEE/ACM Transactions on Networking (TON)
Price-based rate control in random access networks
IEEE/ACM Transactions on Networking (TON)
A survey on networking games in telecommunications
Computers and Operations Research
A cooperative uplink power control scheme for elastic data services in wireless CDMA systems
ACM SIGCOMM Computer Communication Review
Game Theory for Wireless Engineers (Synthesis Lectures on Communications)
Game Theory for Wireless Engineers (Synthesis Lectures on Communications)
WiFi access point pricing as a dynamic game
IEEE/ACM Transactions on Networking (TON)
Flexible Host-Based Handoff Selection for Next Generation Networks
ICN '08 Proceedings of the Seventh International Conference on Networking
Mobility management across hybrid wireless networks: Trends and challenges
Computer Communications
A review of mobility support paradigms for the internet
IEEE Communications Surveys & Tutorials
Using game theory to analyze wireless ad hoc networks
IEEE Communications Surveys & Tutorials
On designing issues of the next generation mobile network
IEEE Network: The Magazine of Global Internetworking
Automated network selection in a heterogeneous wireless network environment
IEEE Network: The Magazine of Global Internetworking
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At present, none of the known mobility management proposals can efficiently support mobile wireless users in the Next Generation Network (NGN) because this environment will have both heterogeneous access technologies belonging to diverse network providers, and users using services with different network requirements. We aim to use Game Theory (GT) to understand how to fulfil the expectations of both users and network providers in a way that the NGN will work efficiently. In this work, we propose a game between the network operator and the users to evaluate how each user's decision impacts on the operator reward, and vice-versa. In addition, we investigate how both players can maximize their rewards or utilities, noticing they have conflicting expectations. Then, we analyse distinct network usage scenarios: no user mobility, mobility detected by both operator and user, distinct user classes and various network loads. Our results show that our mobility model with heterogeneous users has a Nash Equilibrium (NE) but it depends on the network load, who triggers the handover and the handover management approach being used. In our opinion, these results give some guidelines as to how the NGN mobility should be supported.