Dynamic Spectrum Access and the Role of Game Theory

Transcription

1 Dynamic Spectrum Access and the Role of Game Theory Luiz A. DaSilva! Professor of Telecommunications CONNECT, Trinity College Dublin Riunione Annuale 2015 dell'associazione Gruppo Nazionale Telecomunicazioni e Tecnologie dell Informazione (GTTI) L Aquila, Italy, 18 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 Dynamic spectrum access Resource allocation and management Who gets what resources when Incentives Cooperative communications Coexistence (between equals or hierarchical) Pricing How much to charge for resources

5 Game theory A set of analy@cal tools from economics and mathema@cs to predict the outcome of complex interac@ons among ra@onal en@@es

6 Basic concepts Equilibrium concepts Pareto Stackleberg games Bargaining Best/beOer response Disagreement point Mechanism design

7 More sophis4cated game theore4c models Hierarchy of decision makers Stackelberg games Uncertainty as to player types Bayesian games Sub- set of players games SeSng the rules of the game Mechanism design

8 Power control games Consider a network where nodes [players] set transmit power levels autonomously [ac@ons] What is the appropriate u@lity func@on? A func@on of transmit power and SIR/SINR If receivers are modelled as co- located, it is possible to derive the Nash equilibrium However, it is not Pareto efficient A u@lity that also contains a term for per- unit price of transmit power does somewhat beoer Note: posi@ve versus norma@ve models of u@lity

9 Interference avoidance games Consider a CDMA system, with users [players] differen@ated by their choice of spreading codes [ac@ons] The u@lity will be related to the orthogonality of the spreading code selec@ons SINR for a correla@on receiver It turns out that the game can be formulated as an ordinal poten@al game Desirable proper@es of NE Convergence to the NE through beoer response / best response dynamics

10 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

11 Cooperative spectrum sharing N transmioer/receiver pairs [players] Bandwidth B divided into K channels TransmiOers allocate power over the K channels [ac@ons], subject to a maximum power constraint U@lity func@on is the aggregate capacity Depends on interference levels in each channel U@lity space convexifies as K increases Distributed algorithm to arrive at the Nash bargaining solu@on, by exchanging informa@on in the neighbourhood

12 Coalitions for resource sharing N transmioer/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

13 Suppor4ng D2D communica4ons 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

14 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 Mechanism incen@vises truth- telling

15 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?

16 Single-stage, non cooperative Multi-stage games Stackelberg games Cooperative games

17 Coalition formation Mechanism design

18 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.) 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) Models can be applied at different scales: individual transmissions by nodes, longer-term decisions by transmitters or by users, interactions among networks, operators, etc.

19 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