dynamic spectrum access and the role of game theory · 2015. 6. 18. · d2d communication, spectrum...
Post on 02-Jan-2021
0 Views
Preview:
TRANSCRIPT
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
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
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
[Doyle, Forde, Kibilda, DaSilva, Proc. of IEEE, 2014]
Resource allocation and management
Who gets what resources when
Dynamic spectrum access
Incentives Cooperative communications Coexistence (between equals or hierarchical)
Pricing How much to charge for resources
A set of analy@cal tools from economics and mathema@cs to predict the outcome of complex interac@ons among ra@onal en@@es
Game theory
Non-cooperative, cooperative game theory
vs.
Single-stage, multi-stage
Equilibrium concepts Pareto op@mality Stackleberg games Bargaining solu@ons Best/beOer response Disagreement point Mechanism design
Basic concepts
players
A game: +
action sets
+
utility functions
More sophis4cated game theore4c models…
Hierarchy of decision makers Stackelberg games
Uncertainty as to player types Bayesian games
Sub-‐set of players coopera@ng Coali@on games
SeSng the rules of the game Mechanism design
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
Power control games
A candidate utility function [Shah, Mandayam, Goodman, ’98]
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
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
Interference avoidance games
[Menon et al., ’09]
❶ Primary users (PUs) can charge secondary users (SUs) for access to spectrum !❷ SUs distributedly select on which sub-‐bands to operate
Mul@ple SUs can occupy the same sub-‐band and cooperate in communica@ng
!❸ SUs control their transmit power ! Model as inter-‐related Stackelberg game and coali@on forma@on game
! Derive an algorithm to arrive at the NE for the individual games and the SE for the hierarchical game
Hierarchical spectrum sharing
[Xiao, Bi, Niyato, DaSilva, JSAC’12]
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
Cooperative spectrum sharing
[Suris, DaSilva, Han, MacKenzie, Somali, TWC’09]
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
Coalitions for resource sharing
[Khan, Glisic, DaSilva, Lehtomakki, TCIAIG’10]
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
[Xiao, Chen, Yuen, Han, DaSilva, TWC’15]
Suppor4ng D2D communica4ons in cellular bands
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
Matching subscribers to operators
[Xiao, Han, Chen, DaSilva, JSAC’15]
Inter-‐operator sharing and virtualised wireless networks
Games between operators How much infra-‐structure to deploy individually and how much to deploy collec@vely? Spectrum versus infra-‐structure sharing
Games between operators and over-‐the-‐top service providers
Should the OTT deploy its own infra-‐structure?
Single-stage, non cooperative
initial works on power and interference games potential games, reach equilibrium via best response
Multi-stage games
can incorporate rewards, punishment for prior behaviour routing, peer-to-peer services, sharing repeated games, dynamic games, Markov games
Stackelberg games hierarchy in decision-making primary/secondary use of spectrum
Cooperative games opportunity for bargaining spectrum sharing among equals
Coalition formationcooperation enabled with a subset of all players D2D communication, spectrum sharing decisions
Mechanism designdevelopment of incentive-compatible mechanisms spectrum auctions, matching between subscribers and providers
Also: games of imperfect information, games of imperfect monitoring, evolutionary game theory, etc.
• 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.
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. 986-1001, May 2015.
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. 60-73, July 2014.
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. 2053-2069.
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. 17-30.
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. 5225-5237.
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. 46-56, 4th quarter 2005.
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. 7-10
luizdasilva.wordpress.com
top related