Using Game Theory to Model Resource Sharing in Future Wireless Networks

Transcription

1 Using Game Theory to Model Resource Sharing in Future Wireless Networks Luiz A. DaSilva Professor of Telecommunications CONNECT, Trinity College Dublin European Future of Wireless Technology Workshop Stockholm, Sweden, 16 June 2015

2 To evolve future wireless networks: More spectrum (e.g., mm- wave, licensed + unlicensed) More antennas (massive MIMO) More technologies New spectrum licensing regimes Cell densifica@on Sharing of infrastructure, backhaul, processing, storage Virtualised wireless networks

3 Wireless networks of the future will be characterised by heterogeneity of spectrum usage regimes of ownership models of radio access technologies where resources are shared and orchestrated to create bespoke, virtual networks designed for specific services

4 Game theory Predict outcomes of autonomous decision making Design that lead to desirable outcomes Machine learning Guide the decision process of autonomous decision makers Arrive at equilibrium

5 Non- coopera<ve game theory e.g., power control and interference games establish a Nash equilibrium establish a path to the Nash equilibrium (e.g., best response for poten@al games) Coopera<ve game theory e.g., spectrum sharing among equals establish a bargaining solu@on establish a path to the bargaining solu@on

6 More sophis<cated game theore<c models Hierarchy of decision makers Stackelberg games Uncertainty as to player types Bayesian games Sub- set of players games SeRng the rules of the game Mechanism design

7 Hierarchical spectrum sharing ❶ Primary users (PUs) can charge secondary users (SUs) for access to spectrum ❷ SUs distributedly select on which sub- bands to operate SUs can occupy the same sub- band and cooperate in ❸ SUs control their transmit power Model as inter- related Stackelberg game and game Derive an algorithm to arrive at the NE for the individual games and the SE for the hierarchical game

8 Resource- sharing coali<ons N transmiker/receiver pairs [players] Channel selec@on and transmit power [ac@ons] U@lity can include network- wide spectrum efficiency, fairness, network connec@vity Study the coali@on forma@on process and the stability of coali@ons

9 Suppor<ng D2D communica<ons in cellular bands D2D links [players] compete for sub- bands occupied by a cellular subscriber (if interference is tolerable) or for a sub- band for exclusive use (otherwise) Mul@ple D2D links can share a sub- band D2D links do not know about others preferences, loca@on, link condi@ons Bayesian non- transferable u@lity overlapping coali@on forma@on game Propose a hierarchical matching algorithm to achieve a stable, unique matching structure

10 Dynamically matching subscribers to operators Subscribers [players] dynamically request channels of operators Bayesian game: subscribers are unaware of each other s preferences Belief func@ons, learning Matching market: subscribers are matched to operators, then to sub- bands controlled by the operator Design a mechanism that incen@vises truth- telling

11 Inter- operator sharing and virtualised wireless networks Games between operators How much infra- structure to deploy individually and how much to deploy Spectrum versus infra- structure sharing Games between operators and over- the- top service providers Should the OTT deploy its own infra- structure?

12 Game theory is being used to model increasingly complex interactions among autonomous decision makers Models are particularly tailored to autonomous decision making and reasoning by different network entities - in line with trends in wireless networks (HetNets, D2D, resource sharing, etc.) Models can be applied at different scales: individual transmissions by nodes, longer-term decisions by transmitters or by users, interactions among networks, operators, etc. Machine learning meets game theory: some learning processes can be shown to converge to Nash equilibria (e.g., application of learning automata to dynamic channel selection) Stochastic geometry meets game theory: analysis of incentives for inter-operator sharing

13 Y. Xiao, K.-C. Chen, C. Yuen, Z. Han, and L. A. DaSilva, A Bayesian Overlapping Coalition Formation Game for Device-to-Device Spectrum Sharing in Cellular Networks, IEEE Transactions on Wireless Communications, 2015 (to appear). Z. Khan, J. J. Lehtomäki, L. A. DaSilva, E. Hossain, and M. Latva-aho, Opportunistic Channel Selection by Cognitive Wireless Nodes under Imperfect Observations and Limited Memory: A Repeated Game Model, IEEE Transactions on Mobile Computing, 2015 (to appear). Y. Xiao, Z. Han, K.-C. Chen, and L. A. DaSilva, Bayesian Hierarchical Mechanism Design for Cognitive Radio Networks, IEEE Journal on Selected Areas in Communications (JSAC), vol. 33, no. 5, pp , May H. Ahmadi, Y. H. Chew, N. Reyhani, C. C. Chai, and L. A. DaSilva, Learning Solutions for Auction-based Dynamic Spectrum Access in Multicarrier Systems, Computer Networks, vol. 67, pp , July Y. Xiao, G. Bi, D. Niyato, and L. A. DaSilva, A Hierarchical Game Theoretic Framework for Cognitive Radio Networks, IEEE Journal on Selected Areas in Communications (JSAC), vol. 30, no. 10, November 2012, pp Z. Khan, S. Glisic, L. A. DaSilva, and J. Lehtomaki, Modeling the Dynamics of Coalition Formation Games for Cooperative Spectrum Sharing in an Interference Channel, IEEE Trans. on Computational Intelligence and AI in Games, vol. 3, no. 1, Mar. 2011, pp J. E. Suris, L. A. DaSilva, Z. Han, A. B. MacKenzie, and R. S. Komali, Asymptotic Optimality for Distributed Spectrum Sharing Using Bargaining Solutions, IEEE Trans. on Wireless Communications, vol. 8, no. 10, Oct. 2009, pp V. Srivastava, J. Neel, A. MacKenzie, R. Menon, L.A. DaSilva, J. Hicks, J.H. Reed and R. Gilles, Using Game Theory to Analyze Wireless Ad Hoc Networks, IEEE Communications Surveys and Tutorials, vol. 7, no. 4, pp , 4 th quarter I. Macaluso, L. A. DaSilva, and L. E. Doyle, Learning Nash Equilibria in Distributed Channel Selection for Frequency-Agile Radios, ECAI 2012 Workshop on Artificial Intelligence for Telecommunications and Sensor Networks (WAITS), Montpellier, France, August 27-31, 2012, pp luizdasilva.wordpress.com