Hierarchical Network Formation Games in the Uplink of Multi-Hop Wireless Networks

In this paper, we propose a game theoretic approach to tackle the problem of the distributed formation of the hierarchical network architecture that connects the nodes in the uplink of a wireless multi-hop network. Unlike existing literature which focused on the performance assessment of hierarchical multi-hop networks given an existing topology, this paper investigates the problem of the formation of this topology among a number of nodes that seek to send data in the uplink to a central base station through multi-hop. We model the problem as a hierarchical network formation game and we divide the network into different hierarchy levels, whereby the nodes belonging to the same level engage in a non-cooperative Nash game for selecting their next hop. As a solution to the game, we propose a novel equilibrium concept, the hierarchical Nash equilibrium, for a sequence of multi-stage Nash games, which can be found by backward induction analytically. For finding this equilibrium, we propose a distributed myopic dynamics algorithm, based on fictitious play, in which each node computes the mixed strategies that maximize its utility which represents the probability of successful transmission over the multi-hop communication path in the presence of interference. Simulation results show that the proposed algorithm presents significant gains in terms of average achieved expected utility per user up to 125.6% relative to a nearest neighbor algorithm.

[1]  Gabrielle Demange,et al.  A Survey of Network Formation Models: Stability and Efficiency , 2005 .

[2]  Amrita Dhillon,et al.  Group Formation in Economics; Networks, Clubs and Coalition , 2005 .

[3]  Pin-Han Ho,et al.  Relay Station Placement in IEEE 802.16j Dual-Relay MMR Networks , 2008, 2008 IEEE International Conference on Communications.

[4]  William A. Arbaugh,et al.  Dynamic spectrum access in cognitive radio networks , 2006 .

[5]  Ekram Hossain,et al.  Dynamic Spectrum Access and Management in Cognitive Radio Networks , 2009 .

[6]  Jeff S. Shamma,et al.  Unified convergence proofs of continuous-time fictitious play , 2004, IEEE Transactions on Automatic Control.

[7]  Jeroen Kuipers,et al.  Local Dynamics in Network Formation , 2008 .

[8]  T. Başar,et al.  Dynamic Noncooperative Game Theory, 2nd Edition , 1998 .

[9]  John G. Proakis,et al.  Digital Communications , 1983 .

[10]  Pin-Han Ho,et al.  Optimal relay station placement in IEEE 802.16j networks , 2007, IWCMC.

[11]  Edward W. Knightly,et al.  Cooperative Strategies and Optimal Scheduling for Tree Networks , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[12]  Mihaela van der Schaar,et al.  Distributed Resource Management in Multihop Cognitive Radio Networks for Delay-Sensitive Transmission , 2009, IEEE Transactions on Vehicular Technology.

[13]  Zhu Han,et al.  Joint Optimization of Placement and Bandwidth Reservation for Relays in IEEE 802.16j Mobile Multihop Networks , 2009, 2009 IEEE International Conference on Communications.

[14]  M. Jackson A Survey of Models of Network Formation: Stability and Efficiency , 2003 .

[15]  Ying-Dar Lin,et al.  Multihop cellular: a new architecture for wireless communications , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[16]  Roy D. Yates,et al.  A Framework for Uplink Power Control in Cellular Radio Systems , 1995, IEEE J. Sel. Areas Commun..

[17]  Peng Hao,et al.  Enhanced tree routing for wireless sensor networks , 2009, Ad Hoc Networks.

[18]  D. Fudenberg,et al.  The Theory of Learning in Games , 1998 .

[19]  T. Başar,et al.  Dynamic Noncooperative Game Theory , 1982 .