A game approach for cell selection and resource allocation in heterogeneous wireless networks

Cell selection and resource allocation (CS-RA) are processes of determining cell and radio resource which provide service to mobile station (MS). Optimizing these processes is an important step towards maximizing the utilization of current and future networks. In this paper, we investigate the problem of CS-RA in heterogeneous wireless networks. Specifically, we propose a distributed cell selection and resource allocation mechanism, in which the CS-RA processes are performed by MSs independently. We formulate the problem as a two-tier game named as inter-cell game and intra-cell game, respectively. In the first tier, i.e. the inter-cell game, MSs select the best cell according to an optimal cell selection strategy derived from the expected payoff. In the second tier, i.e., the intra-cell game, MSs choose the proper radio resource in the serving cell to achieve maximum payoff. We analyze the existence of Nash equilibria of both games, the structure of which suggests the interesting property that we can achieve automatic load balance through the two-tier games. Furthermore, we propose distributed algorithms named as CS-Algorithm and RA-Algorithm to enable the independent MSs converge to Nash equilibria. Simulation results show that the proposed algorithms converge effectively to Nash equilibria and that the proposed CS-RA mechanism achieves better performance in terms of throughput and payoff compared to conventional mechanisms.

[1]  Cell Selection in 4 G Cellular Networks December 23 , 2007 1 Problem definition , 2007 .

[2]  Abbas Jamalipour,et al.  Wireless communications , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[3]  George T. Karetsos,et al.  A hierarchical radio resource management framework for integrating WLANs in cellular networking environments , 2005, IEEE Wireless Communications.

[4]  Dusit Niyato,et al.  Call admission control for QoS provisioning in 4G wireless networks: issues and approaches , 2005, IEEE Network.

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

[6]  Youyun Xu,et al.  Multi-radio Channel Allocation in Multi-hop Wireless Networks , 2009 .

[7]  Xinbing Wang,et al.  Speed Improves Delay-Capacity Trade-Off in MotionCast , 2011, IEEE Transactions on Parallel and Distributed Systems.

[8]  Dusit Niyato,et al.  A Noncooperative Game-Theoretic Framework for Radio Resource Management in 4G Heterogeneous Wireless Access Networks , 2008, IEEE Transactions on Mobile Computing.

[9]  Reuven Bar-Yehuda,et al.  Cell Selection in 4G Cellular Networks , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[10]  Khaled Ben Letaief,et al.  Multiuser OFDM with adaptive subcarrier, bit, and power allocation , 1999, IEEE J. Sel. Areas Commun..

[11]  Saurabh Ganeriwal,et al.  On selfish behavior in CSMA/CA networks , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[12]  Janise McNair,et al.  Vertical handoffs in fourth-generation multinetwork environments , 2004, IEEE Wireless Communications.

[13]  L. Hanzo,et al.  Adaptive multicarrier modulation: a convenient framework for time-frequency processing in wireless communications , 2000, Proceedings of the IEEE.

[14]  Tiankui Zhang,et al.  Uplink Multi-Cell Non-Cooperative Power Allocation Game Algorithm for OFDMA Cellular Networks , 2009, 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing.

[15]  Rudolf Mathar,et al.  Integrated Optimal Cell Site Selection and Frequency Allocation for Cellular Radio Networks , 2002, Telecommun. Syst..

[16]  Michael L. Honig,et al.  Auction-Based Spectrum Sharing , 2006, Mob. Networks Appl..

[17]  Kwang Bok Lee,et al.  Transmit power adaptation for multiuser OFDM systems , 2003, IEEE J. Sel. Areas Commun..

[18]  W. Benjapolakul,et al.  Fair-efficient threshold parameters selection in call admission control for CDMA mobile multimedia communications using game theoretic framework , 2005, Second IEEE Consumer Communications and Networking Conference, 2005. CCNC. 2005.

[19]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.

[20]  E. Maasland,et al.  Auction Theory , 2021, Springer Texts in Business and Economics.

[21]  Xiaodong Wang,et al.  Coordinated load balancing, handoff/cell-site selection, and scheduling in multi-cell packet data systems , 2008, Wirel. Networks.

[22]  Michael L. Honig,et al.  Forward-link resource allocation for a two-cell voice network with multiple service classes , 2003, 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003..

[23]  Xinbing Wang,et al.  Throughput and delay scaling of general cognitive networks , 2011, 2011 Proceedings IEEE INFOCOM.

[24]  Stephen V. Hanly,et al.  An Algorithm for Combined Cell-Site Selection and Power Control to Maximize Cellular Spread Spectrum Capacity (Invited Paper) , 1995, IEEE J. Sel. Areas Commun..

[25]  Rajesh Sundaresan,et al.  Uplink power control and base station association in multichannel cellular networks , 2009, 2009 International Conference on Game Theory for Networks.

[26]  Andrea Goldsmith,et al.  Wireless Communications , 2005, 2021 15th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS).

[27]  M. Dufwenberg Game theory. , 2011, Wiley interdisciplinary reviews. Cognitive science.

[28]  Michael L. Honig,et al.  Two-cell power allocation for downlink CDMA , 2004, IEEE Transactions on Wireless Communications.

[29]  Zhigang Cao,et al.  Channel estimation for OFDM transmission in multipath fading channels based on parametric channel modeling , 2001, IEEE Trans. Commun..

[30]  H. Vincent Poor,et al.  A game-theoretic approach to energy-efficient power control in multicarrier CDMA systems , 2006, IEEE Journal on Selected Areas in Communications.

[31]  A. Girotra,et al.  Performance Analysis of the IEEE 802 . 11 Distributed Coordination Function , 2005 .