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Non-Cooperative Behavior in Wireless Networks

Non-Cooperative Behavior in Wireless Networks. Márk Félegyházi (EPFL). PhD. defense – April 2007. Prospective wireless networks. Relaxing spectrum licensing: small network operators in unlicensed bands inexpensive access points flexible deployment community and ad hoc networks

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Non-Cooperative Behavior in Wireless Networks

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  1. Non-Cooperative Behavior in Wireless Networks Márk Félegyházi (EPFL) PhD. defense – April 2007

  2. Prospective wireless networks Relaxing spectrum licensing: • small network operators in unlicensed bands • inexpensive access points • flexible deployment • community and ad hoc networks • no authority • peer-to-peer network operation • cognitive radio • restricted operation in any frequency band • no interference with licensed (primary) users • adaptive behavior Márk Félegyházi (EPFL) - PhD defense

  3. Motivation • more complexity at the network edges • decentralization • ease of programming for wireless devices • rational users  • more adaptive wireless devices • potential selfish behaviorof devices TRENDS OUTCOME What is the effect of selfish behavior in wireless networks? Márk Félegyházi (EPFL) - PhD defense

  4. Game theory in networking • Peer-to-peer networks • free-riding [Golle et al. 2001, Feldman et al. 2007] • trust modeling [Aberer et al. 2006] • Wired networks • congestion pricing [Korilis et al. 1995, Korilis and Orda 1999, Johari and Tsitsiklis 2004] • bandwidth allocation [Yaïche et al. 2000] • coexistence of service providers [Shakkottai and Srikant 2005/2006, He and Walrand 2006] • Wireless networks • power control [Goodman and Mandayam 2001, Alpcan et al. 2002, Xiao et al. 2003] • resource/bandwidth allocation [Marbach and Berry 2002, Qui and Marbach 2003] • medium access [MacKenzie and Wicker 2003, Yuen and Marbach 2005, Čagalj et al. 2005] • Wi-Fi pricing [Musacchio and Walrand 2004/2006] Márk Félegyházi (EPFL) - PhD defense

  5. Outline of the thesis Part I: Introduction to game theory • Ch 1: A tutorial on game theory • Ch. 2: Multi-radio channel allocation in wireless networks • Ch. 3: Packet forwarding in static ad-hoc networks • Ch. 4: Packet forwarding in dynamic ad-hoc networks • Ch. 5: Packet forwarding in multi-domain sensor networks • Ch. 6: Cellular operators in a shared spectrum • Ch. 7: Border games in cellular networks Part II: Non-cooperative users Part III: Non-cooperative network operators Márk Félegyházi (EPFL) - PhD defense

  6. Part II: Non-Cooperative Users Chapter 2: Multi-Radio Channel Allocation in Wireless Networks

  7. Related Work • Channel allocation • in cellular networks: fixed and dynamic: [Katzela and Naghshineh 1996, Rappaport 2002] • in WLANs [Mishra et al. 2005] • in cognitive radio networks [Zheng and Cao 2005] • Multi-radio networks • mesh networks [Adya et al. 2004, Alicherry et al. 2005] • cognitive radio [So et al. 2005] • Competitive medium access • Aloha [MacKenzie and Wicker 2003, Yuen and Marbach 2005] • CSMA/CA [Konorski 2002, Čagalj et al. 2005] • WLAN channel coloring [Halldórsson et al. 2004] • channel allocation in cognitive radio networks [Cao and Zheng 2005, Nie and Comaniciu 2005] Márk Félegyházi (EPFL) - PhD defense

  8. Problem • multi-radio devices • set of available channels How to assign radios to available channels? Márk Félegyházi (EPFL) - PhD defense

  9. System model (1/3) • C – set of orthogonal channels (|C| = C) • N – set of communicating pairs of devices (|N| = N) • sender and receiver are synchronized • single collision domain if they use the same channel • devices have multiple radios • k radios at each device, k ≤ C Márk Félegyházi (EPFL) - PhD defense

  10. System model (2/3) • channels with the same properties • τ()– total throughput on any channel x 1 number of links Márk Félegyházi (EPFL) - PhD defense

  11. System model (3/3) • N communicating pairs of devices • C orthogonal channels • k radios at each device (k links for each pair) number of links by pair i on channel x → Intuition: example: multiple communication links on one channel ? Márk Félegyházi (EPFL) - PhD defense

  12. Multi-radio channel allocation (CA) game • selfish users (communicating pairs) • non-cooperative game GCA • players→ communicating pairs • strategy → channel allocation • payoff → total throughput • strategy: • strategy matrix: • payoff: Márk Félegyházi (EPFL) - PhD defense

  13. Use of all radios p4 p4 Lemma: If S* is a NE in GCA, then . Each player should use all of his radios. Intuition: Player i is always better of deploying unused radios. all channel allocations Lemma Márk Félegyházi (EPFL) - PhD defense

  14. Load-balancing channel allocation • Consider two arbitrary channels x and y, where ky ≥ kx • distance: dy,x = ky – kx Proposition: If S* is a NE in GCA, then dy,x≤ 1, for any channel x and y. NE candidate: all channel allocations Lemma Proposition Márk Félegyházi (EPFL) - PhD defense

  15. Nash equilibria (1/2) p2 p4 • Consider two arbitrary channels x and y, where ky ≥ kx • distance: dy,x = ky – kx Theorem (case 1):If for any two channels x and y in C it is true that ki,x≤ 1,for all iand dy,x≤ 1, then S* is a Nash equilibrium. Nash Equilibrium: Use one link per channel. all channel allocations NE case 1 Lemma Proposition Márk Félegyházi (EPFL) - PhD defense

  16. Nash equilibria (2/2) • Consider two arbitrary channels x and y, where ky ≥ kx channels with the minimum/maximum number of links dy,x = ky – kx di,y,x = ki,y – ki,x → Theorem (case 2):If dy,x≤ 1 for x,yin C andthere exists j in N and x’ in Cmin such that kj,x’> 1, in addition kj,y’ ≤ 1 for all y’inCmax and di,x’,x’’≤ 1 for any x’,x’’ in Cmin, then S* is a Nash equilibrium. Use multiple links on certain channels. Nash Equilibrium: all channel allocations NE case 1 Lemma Proposition Márk Félegyházi (EPFL) - PhD defense NE case 2

  17. Efficiency (1/2) Theorem: In GCA, the price of anarchy is: where Corollary: If the throughput function τ() is constant (ex. theoretical CSMA/CA), then any Nash equilibrium channel allocation is Pareto-optimal in GCA. Márk Félegyházi (EPFL) - PhD defense

  18. Efficiency (2/2) • CSMA/CA protocol • In theory, the throughput function τ() is constant POA = 1 • In practice, there are collisions, but τ() decreases slowly with kx (due to the RTS/CTS method) G. Bianchi, “Performance Analysis of the IEEE 802.11 Distributed Coordination Function,” in IEEE Journal on Selected Areas of Communication (JSAC), 18:3, Mar. 2000 Márk Félegyházi (EPFL) - PhD defense

  19. Convergence to NE (1/3) Algorithm with imperfect info: • move links from “crowded” channels to other randomly chosen channels • desynchronize the changes • convergence is not ensured N = 5, C = 6, k = 3 p5 p4 p5 p4 p3 p4 p3 p3 p2 p5 p1 p2 p2 p1 p1 time p5: c2→c5 p1: c4→c6 c4 c5 channels c6 c1 c2 c3 p1 p5 c6→c4 c5→c2 p4 p3 p3: c2→c5 p4: idle p2 c6→c4 c1→c3 p1 p2: c2→c5 p1: c2→c5 c6→c4 Márk Félegyházi (EPFL) - PhD defense

  20. Convergence to NE (2/3) Algorithm with imperfect info: move links from “crowded” channels to other randomly chosen channels desynchronize the changes convergence is not ensured Balance: best balance (NE): unbalanced (UB): Efficiency: Márk Félegyházi (EPFL) - PhD defense

  21. Convergence to NE (3/3) Márk Félegyházi (EPFL) - PhD defense

  22. Summary – Non-cooperative users • wireless networks with multi-radio devices • users of the devices are selfish players • GCA – channel allocation game • results for a Nash equilibrium: • players should use all their radios • load-balancing channel allocation • two cases of Nash equilibria • NE are efficient both in theory and practice • fairness issues • coalition-proof equilibria • algorithms to achieve efficient NE: • centralized algorithm with perfect information • distributed algorithm with imperfect information Márk Félegyházi (EPFL) - PhD defense

  23. Part III: Non-CooperativeNetwork Operators Chapter 7: Border Games in Cellular Networks

  24. Related Work • Power control in cellular networks • up/downlink power control in CDMA [Hanly and Tse 1999, Baccelli et al. 2003, Catrein et al. 2004] • pilot power control in CDMA [Kim et al. 1999, Värbrand and Yuan 2003] • using game theory [Alpcan et al. 2002, Goodman and Mandayam 2001, Ji and Huang 1998, Meshkati et al. 2005, Lee et al. 2002] • Coexistence of service providers • wired [Shakkottai and Srikant 2005, He and Walrand 2006] • wireless [Shakkottai et al. 2006, Zemlianov and de Veciana 2005] Márk Félegyházi (EPFL) - PhD defense

  25. Problem • spectrum licenses do not regulate access over national borders • adjust pilot power to attract more users Is there an incentive for operators to apply competitive pilot power control? Márk Félegyházi (EPFL) - PhD defense

  26. System model (1/2) Network: • cellular networks using CDMA • channels defined by orthogonal codes • two operators: A and B • one base station each • pilot signal power control Users: • roaming users • users uniformly distributed • select the best quality BS • selection based signal-to-interference-plus-noise ratio (SINR) Márk Félegyházi (EPFL) - PhD defense

  27. System model (2/2) TAw pilot signal SINR: TBw TAv PB PA v B A Pi – pilot power of i – processing gain for the pilot signal – channel gain between BS i and user v traffic signal SINR: – noise energy per symbol – available bandwidth – own-cell interference affecting the pilot signal – own-cell interference factor – traffic power between BS i and user v – set of users attached to BS i – other-to-own-cell interference factor Márk Félegyházi (EPFL) - PhD defense

  28. Game-theoretic model Power Control Game, GPC players → networks operators (BSs), A and B strategy → pilot signal power, 0W < Pi < 10W, i = {A, B} standard power, PS = 2W payoff → profit, where is the expected income serving user v normalized payoff difference: Márk Félegyházi (EPFL) - PhD defense

  29. Simulation Márk Félegyházi (EPFL) - PhD defense

  30. Is there a game? • only A is strategic (B uses PB = PS) • 10 data users • path loss exponent, α = 2 Δi Márk Félegyházi (EPFL) - PhD defense

  31. Strategic advantage • normalized payoff difference: Márk Félegyházi (EPFL) - PhD defense

  32. Payoff of A • Both operators are strategic • path loss exponent, α = 4 Márk Félegyházi (EPFL) - PhD defense

  33. Nash equilibrium • unique NE • NE power P* is higher than PS Márk Félegyházi (EPFL) - PhD defense

  34. Efficiency zero-sum game • 10 data users Márk Félegyházi (EPFL) - PhD defense

  35. Convergence to NE (1/2) • convergence based on better-response dynamics • convergence step: 2 W PA = 6.5 W Márk Félegyházi (EPFL) - PhD defense

  36. Convergence to NE (2/2) • convergence step: 0.1 W Márk Félegyházi (EPFL) - PhD defense

  37. Summary – Non-cooperative network operators • two operators on a national border • single-cell model • pilot power control • roaming users • power control game, GPC • operators have an incentive to be strategic • NE are efficient, but they use high power • simple convergence algorithm • extended game with power cost • Prisoner’s Dilemma Márk Félegyházi (EPFL) - PhD defense

  38. Summary

  39. Thesis contributions (Ch. 1: A tutorial on game theory) • facilitate the application of game theory in wireless networks M. Félegyházi and J.-P. Hubaux, “Game Theory in Wireless Networks: A Tutorial,” submitted to ACM Communication Surveys, 2006 Márk Félegyházi (EPFL) - PhD defense

  40. Thesis contributions(Ch. 2: Multi-radio channel allocation in wireless networks) • NE are efficient and sometimes fair, and they can be reached even if imperfect information is available • load-balancing Nash equilibria • each player has one radio per channel • some players have multiple radios on certain channels • NE are Pareto-efficient both in theory and practice • fairness issues • coalition-proof equilibria • convergence algorithms to efficient NE M. Félegyházi, M. Čagalj, S. S. Bidokhti, and J.-P. Hubaux, “Non-cooperative Multi-radio Channel Allocation in Wireless Networks,” in Proceedings of Infocom 2007, Anchorage, USA, May 6-12, 2007 Márk Félegyházi (EPFL) - PhD defense

  41. Thesis contributions(Ch. 3: Packet forwarding in static ad-hoc networks) • incentives are needed to promote cooperation in ad hoc networks • model and meta-model using game theory • dependencies / dependency graph • study of NE • in theory, NE based on cooperation exist • in practice, the necessary conditions for cooperation do not hold • part of the network can still cooperate M. Félegyházi, L. Buttyán and J.-P. Hubaux, “Nash Equilibria of Packet Forwarding Strategies in Wireless Ad Hoc Networks,” in Transactions on Mobile Computing (TMC), vol. 5, nr. 5, May 2006 Márk Félegyházi (EPFL) - PhD defense

  42. Thesis contributions(Ch. 4: Packet forwarding in dynamic ad-hoc networks) • mobility helps cooperation in ad hoc networks • spontaneous cooperation exists on a ring (theoretical) • cooperation resistant to drift (alternative cooperative strategies) to some extent • in reality, generosity is needed • as mobility increases, less generosity is needed M. Félegyházi, L. Buttyán and J.-P. Hubaux, “Equilibrium Analysis of Packet Forwarding Strategies in Wireless Ad Hoc Networks - the Dynamic Case,” Technical report - LCA-REPORT-2003-010, 2003 Márk Félegyházi (EPFL) - PhD defense

  43. Thesis contributions(Ch. 5: Packet forwarding in multi-domain sensor networks) • sharing sinks is beneficial and sharing sensors is also in certain scenarios • energy saving gives a natural incentive for cooperation • sharing sinks • with common sinks, sharing sensors is beneficial • in sparse networks • in hostile environments M. Félegyházi, L. Buttyán and J.-P. Hubaux, “Cooperative Packet Forwarding in Multi-Domain Sensor Networks,” in PerSens 2005, Kauai, USA, March 8, 2005 Márk Félegyházi (EPFL) - PhD defense

  44. Thesis contributions(Ch. 6: Cellular operators in a shared spectrum) • both cooperation (low powers) and defection (high powers) exist, but cooperation can be enforced by punishments • wireless operators compete in a shared spectrum • single stage game • various Nash equilibria in the grid scenario, depending on cooperation parameters • repeated game • RMIN (cooperation) is enforceable with punishments • general scenario = arbitrary ranges • the problem is NP-complete M. Félegyházi and J.-P. Hubaux, “Wireless Operators in a Shared Spectrum,” in Proceedings of Infocom 2006, Barcelona, Spain, April 23-29, 2006 Márk Félegyházi (EPFL) - PhD defense

  45. Thesis contributions(Ch. 7: Border games in cellular networks) • operators have an incentive to adjust their pilot power on the borders • competitive power control on a national border • power control game • operators have an incentive to be strategic • NE are efficient, but they use high power • simple convergence algorithm • extended game corresponds to the Prisoner’s Dilemma M. Félegyházi, M. Čagalj, D. Dufour, and J.-P. Hubaux, “Border Games in Cellular Networks,” in Proceedings of Infocom 2007, Anchorage, USA, May 6-12, 2007 Márk Félegyházi (EPFL) - PhD defense

  46. Selected publications (à la Prof. Gallager) • M. Félegyházi, M. Čagalj, S. S. Bidokhti, and J.-P. Hubaux, “Non-Cooperative Multi-Radio Channel Allocation in Wireless Networks,” in Infocom 2007 • M. Félegyházi, M. Čagalj, D. Dufour, and J.-P. Hubaux, “Border Games in Cellular Networks,” in Infocom 2007 • M. Félegyházi, L. Buttyán and J.-P. Hubaux, “Nash Equilibria of Packet Forwarding Strategies in Wireless Ad Hoc Networks,” in IEEE Transactions on Mobile Computing (TMC), vol. 5, nr. 5, 2006 Márk Félegyházi (EPFL) - PhD defense

  47. Future research directions (1/3) • Cognitive networks • Chapter 2: multi-radio channel allocation • adaptation is a fundamental property of cognitive devices • selfishness is threatening network performance • primary (licensed) users • secondary (cognitive) users • incentives are needed to prevent selfishness • frequency allocation • interference control submitted: M. Félegyházi, M. Čagalj and J.-P. Hubaux, “Efficient MAC in Cognitive Radio Systems: A Game-Theoretic Approach,” submitted to IEEE JSAC, Special Issue on Cognitive Radios, 2008 Márk Félegyházi (EPFL) - PhD defense

  48. Future research directions (2/3) • Coexistence of wireless networks • Chapter 6 and 7: wireless operators in shared spectrum • advancement of wireless technologies • alternative service providers • small operators • social community networks • competition becomes more significant • coexistence results in nonzero-sum games • mechanism to enforce cooperation • competition improves services in preparation: M. H. Manshaei, M. Félegyházi, J. Freudiger, J.-P. Hubaux, and P. Marbach, “Competition of Wireless Network Operators and Social Networks,” to be submitted in 2007 Márk Félegyházi (EPFL) - PhD defense

  49. Future research directions (3/3) • Economics of security and privacy • cryptographic building blocks are quite reliable (some people might disagree) • implementation fails due to economic reasons (3C) • confusion in defining security goals • cost of implementation • complexity of usage • privacy is often not among the security goals • incentives to implement correct security measures • share liabilities • better synchronization • collaboration to prevent attacks submitted: J. Freudiger, M. Raya, M. Félegyházi, and J.-P. Hubaux, “On Location Privacy in Vehicular Mix-Networks,” submitted to Privacy Enhancing Technologies 2007 Márk Félegyházi (EPFL) - PhD defense

  50. Extensions

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