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Game Theory-Based Network Selection: Solutions and Challenges PowerPoint Presentation
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Game Theory-Based Network Selection: Solutions and Challenges

Game Theory-Based Network Selection: Solutions and Challenges

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Game Theory-Based Network Selection: Solutions and Challenges

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  1. Game Theory-Based Network Selection: Solutions and Challenges Speaker: Kai-Wei Ping Advisor: Prof Dr. Ho-Ting Wu 2013/05/20

  2. Outline • Introduction • Network selection problem • Decision Making Process • Decision Criteria • Decision Making • Game Theory • Challenges in Game Theory and 4G • Conclusions and future research directions

  3. Introduction • Smart mobile computing devices have become increasingly affordable and powerful. • No single network technology will be equipped to deal with this explosion of data, making the coexistence of multiple radio access technologies (RATs) a necessity • The focus of this paper is to provide a comprehensive survey of the current research on game theory approaches in relation to network selection solutions

  4. Network selection problem • The next generation of wireless networks is represented as a heterogeneous environment with a number of overlapping RANs • The user device faces the problem of selecting from a number of RANs that differ in technology, coverage, bandwidth, latency, pricing scheme, etc., belonging to the same or different service providers

  5. How • How can an ordinary user, without any background knowledge in wireless networks, know which is the best deal for him?

  6. Network selection problem

  7. Standards which support Network Selection • IEEE 802.21 – The standard enables the optimization of handover between heterogeneous IEEE 802networks and facilitates handover between IEEE 802 networks and cellular networks • Access Network Discovery and Selection Function (ANDSF) – Provides information about the neighbouring access networks to the mobile device through Discovery Information and assists the device in the handover process through rule based network selection policies

  8. Decision Making Process

  9. Decision Criteria • Network metrics – includes information about the technical characteristics or performance of the access networks • Device related – includes information about the end-users’terminaldevice characteristics • Application Requirements – includes information about the requirements needed in order to provide a certain service to the end user • User Preferences – includes information related to the end-users’ satisfaction

  10. Decision Making • The Simple Additive Weighting Method (SAW) – • The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) – • The Multiplicative Exponential Weighting Method(MEW) –

  11. Decision Making • The Elimination and Choice Expressing Reality (ELECTRE)method– concordance set (CSet) and discordance set (DSet) , Cthresholdand Dthreshold. • Analytic Hierarchy Process (AHP) and Grey Relational Analysis (GRA)– • Analytic Hierarchy Process (AHP): The idea behind AHP is to decompose a complicated problem into a hierarchy of simple and easy to solve sub-problems • Grey Relational Analysis (GRA): The GRA method is used to rank candidate networks and select the one which has the highest rank

  12. Game Theory • Game theory is a mathematical tool used in understanding and modelling competitive situations which imply the interaction of rational decision makers with mutual and possibly conflicting interests. • It was originally adopted in economics, in order to model the competition between companies.

  13. Glossary • Player: A player is an agent who makes decisions in a game. • Strategy:In a game in strategic form, a strategy is one of the given possible actions of a player • Payoff: A payoff is a number, also called utility, that reflects the desirability of an outcome to aplayer, for whatever reason • Rationality:A player is said to be rational if he seeks to play in a manner which maximizes his ownpayoff. It is often assumed that the rationality of all players is common knowledge.

  14. Game Component

  15. Nash Equilibrium • The combination of best strategies for each player is known as equilibrium. • When each player cannot benefit anymore by changing his strategy while keeping the other players’ strategies unchanged, then we say hat the solution of the game represents Nash Equilibrium.

  16. Pareto Optimality • When the payoffs cannot be further enhanced with any other strategy combination, the game is said to have reached a Pareto Optimal Nash Equilibrium

  17. Game Theoretic Models • Strategic Game: Prisoner’s Dilemma • Repeated Game • Bargaining Game • Trading Market • Auction Game • Cournot Game • Bankruptcy Game • Stackelberg Game / Leader-Follower Game • Bayesian Game • Coalition Game • Evolutionary Games • Mechanism Design

  18. Game Theory to Network Selection Mapping

  19. Summary of surveyed approaches

  20. Summary of surveyed approaches

  21. Summary of surveyed approaches

  22. Case • Source : “4G Converged Environment: Modeling Network Selection as a Game” • Game model : Strategic Game • Objective : network selection - select the best network to satisfy a service request • Strategy set : the service requests • Payoff : Utility function • Parameter : delay, jitter • Resource : bandwidth • RAT : 4G system

  23. Example • define the network selection game as: • N={1,2}, R={1,2,3,4,5,6}, S1={1,2,3,4,5,6} and S2={1,2,3,4,5,6}. • is the value assigned to network ifor choosing service requestj

  24. Example • Therefore, at the end of the game A1 = {6, 5, 4} and A2 = {1, 2, 3}. • The payoff to both networks is equal to 15; • 6 each from the first round, 5 each from the second and 4 each from the third

  25. Challenges in Game Theory and 4G

  26. Challenges in Game Theory and 4G • Cooperative or Non-cooperative Approach – The 4G environment aims to provide a combination of network and terminal heterogeneity as well as heterogeneous services • Payoffs/Utility Functions – The choice of payoff or utility function is another challenge as it impacts on how the players will choose their actions

  27. Challenges in Game Theory and 4G • Multi-Operator and Multi-Technology – when designing a cooperative or a non-cooperative game, comes when considering a single or multiple operators • Pricing and Billing – Multiple service providers , Multiple RATs

  28. Challenges in Game Theory and 4G • Users’ Implication – If not the best one to the customer, service providers should know what each customer really needs and where the real problem lies • Energy Consumption – • When considering the energy consumption of a multi-interface mobile device, an important aspect is the connectivity • Solution : Cooperative Network protocol (CONET)

  29. Challenges in Game Theory and 4G • Complexity and Real World Scenarios – • In a real world scenario, considering the competitive market, operators will not be willing to provide such information without having a clear benefit from doing so. • Another important aspect when using game theory and dealing with such a heterogeneous and complex environment is the risk of users misbehaving, acting selfishly by trying to obtain the maximum performance over other users, leading to an overall system performance degradation

  30. Conclusions and future research directions • This article aims to familiarize the readers with the network selection concept and with the different game theoretic approaches used in the literature to model the network selection problem. • As game theory is often used to study this interaction between rational decision makers, it makes it applicable in the area of network selection strategies. • An important open issue is the impact of computational complexity of the existing solutions

  31. References • Ramona Trestian, Olga Ormond, and Gabriel-MiroMuntean, ” Game Theory-Based Network Selection: Solutions and Challenges,” in IEEE Communications Surveys & Tutorials, vol. 14, no. 4, pp. 1212 - 1231, 2012. • J. Antoniou and A. Pitsillides, ”4G Converged Environment: Modeling Network Selection as a Game,” in the 16th IST Mobile and Wireless Communications Summit, 2007.