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

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]

Game  theory  !

Predict  outcomes  of  autonomous  decision  making  Design  e@queKes/rules  that  lead  to  desirable  outcomes

Machine  learning  !

Guide  the  decision  process  of  autonomous  decision  makers  Arrive  at  equilibrium

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

More  sophis<cated  game  theore<c  models…

Hierarchy  of  decision  makers  Stackelberg  games

Uncertainty  as  to  player  types  Bayesian  games

Sub-­‐set  of  players  coopera@ng  Coali@on  games

SeRng  the  rules  of  the  game  Mechanism  design

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

[Xiao, Bi, Niyato, DaSilva, JSAC’12]

Hierarchical  spectrum  sharing

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

[Khan, Glisic, DaSilva, Lehtomakki, TCIAIG’10]

Resource-­‐sharing  coali<ons

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]

Suppor<ng  D2D  communica<ons  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  Design  a  mechanism  that  incen@vises  truth-­‐telling

[Xiao, Han, Chen, DaSilva, JSAC’15]

Dynamically  matching  subscribers  to  operators

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?

• 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

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

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