Spectrum utilization using game theory

Spectrum utilization is the most recent communications issue which takes great deal of attention from communication researchers where most of the efforts have been dedicated for spectral efficient utilization. Spectrum sharing is one of the solutions considered in the problem of lack of available frequency for new communication services which are unlicensed. In this work we propose an optimal method for spectrum utilization to increase spectral efficiency. It considers the problem of spectrum holes found in Primary User's (PU) band and detected using one of the spectral sensing methods. The solution is formulated with the help of Game theory approach in such a way that the primary user who has unoccupied frequency can share it with a group of secondary users (SU) in a competitive way. One of the SUs will be a secondary primary user (SPU), share available frequency from PU then offer his sharing to serve other SUs in different rate of sharing. Each user in the group of secondary users has a chance to be secondary primary user depending on reputation of each SU. Enhancing reputation is the only way for any SU to assure a share in the spectrum where it considered the factor of increasing or decreasing rate of sharing as well as factor of being SPU or an ordinary SU. A theoretical non-cooperative game model is introduced in a comparison with a proposed non-dynamic technique which depends on number of subscribers who occupy frequency in each time period. Multiusers compete on sharing the frequency from one of the users who offers sharing at a time when he has low number of subscribers that occupy his band. It is found that non-dynamic sharing results in inefficient spectrum utilization which is one of the reasons of spectrum scarcity where this resource is allocated in fixed way. Spectrum sharing using game theory solves this problem by its ability to make users compete to gain highest rate of spectrum allocation according to the real requirement of each user at each time interval. The problem of urgent case is also discussed when the primary user comes back to using his band which is the specific band of sharing with the secondary users group. SPU makes it easy to unload the required band from multiusers because PU does not need to request his band from each SU in the group. Spectrum Utilization Using Game Theory II

[1]  Jean-Pierre Hubaux,et al.  Game Theory in Wireless Networks: A Tutorial , 2006 .

[2]  Przemyslaw Pawelczak,et al.  Multinode Spectrum Sensing Based on Energy Detection for Dynamic Spectrum Access , 2008, VTC Spring 2008 - IEEE Vehicular Technology Conference.

[3]  Mohamed-Slim Alouini,et al.  On the Energy Detection of Unknown Signals Over Fading Channels , 2007, IEEE Transactions on Communications.

[4]  Marja Matinmikko,et al.  Cognitive radio: An intelligent wireless communication system , 2008 .

[5]  Patrick Mitran,et al.  Limits on communications in a cognitive radio channel , 2006, IEEE Communications Magazine.

[6]  Amitabh Mishra,et al.  A look ahead scheme for adaptive spectrum utilization , 2003, Radio and Wireless Conference, 2003. RAWCON '03. Proceedings.

[7]  Rolf Zeller,et al.  Developmental biology: First come, first served , 2002, Nature.

[8]  H. Tang,et al.  Some physical layer issues of wide-band cognitive radio systems , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[9]  Paul Cotae,et al.  Cognitive Radio: Time Domain Spectrum Allocation using Game Theory , 2007, 2007 IEEE International Conference on System of Systems Engineering.

[10]  Hamed S. Al-Raweshidy,et al.  Optimized Handover Schemes over WiMAX , 2009, WMNC/PWC.

[11]  张哉根,et al.  Leu-M , 1991 .

[12]  Brian Choi,et al.  Distributed Spectrum Sensing for Cognitive Radio Systems , 2007, 2007 Information Theory and Applications Workshop.

[13]  Hamed S. Al-Raweshidy,et al.  Competitive Spectrum Sharing in Wireless Networks: A Dynamic Non-cooperative Game Approach , 2009, WMNC/PWC.

[14]  Huaiyu Dai,et al.  Quickest spectrum sensing in cognitive radio , 2008, 2008 42nd Annual Conference on Information Sciences and Systems.

[15]  H. Saarnisaari,et al.  Spectrum Sensingwith Forward Methods , 2006, MILCOM 2006 - 2006 IEEE Military Communications conference.

[16]  Panagiotis Papadimitratos,et al.  A bandwidth sharing approach to improve licensed spectrum utilization , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[17]  Venugopal V. Veeravalli,et al.  Cooperative Spectrum Sensing and Detection for Cognitive Radio , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[18]  Lang Tong,et al.  A Measurement-Based Model for Dynamic Spectrum Access in WLAN Channels , 2006, MILCOM 2006 - 2006 IEEE Military Communications conference.

[19]  Randall S. Janka,et al.  SYSTEM CONSIDERATIONS FOR AUTONOMOUS DYNAMIC SPECTRUM UTILIZATION , 2005 .

[20]  Dale Hatfield,et al.  Measures of Spectral Efficiency in Land Mobile Radio , 1977, IEEE Transactions on Electromagnetic Compatibility.

[21]  Huseyin Arslan,et al.  Cognitive radio, software defined radio, and adaptiv wireless systems , 2007 .

[22]  Brian L. Mark,et al.  Modeling and analysis of interference in Listen‐Before‐Talk spectrum access schemes , 2006, Int. J. Netw. Manag..

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

[24]  J. Bates,et al.  Ultra sensitive TV detector measurements , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[25]  Allen B. MacKenzie,et al.  Game Theory for Wireless Engineers , 2006, Game Theory for Wireless Engineers.

[26]  Pascal Bianchi,et al.  Cooperative spectrum sensing using random matrix theory , 2008, 2008 3rd International Symposium on Wireless Pervasive Computing.

[27]  R. Young,et al.  Economies of Scale , 1987 .

[28]  Carsten F. Ball,et al.  Spectrum efficiency evaluation for different wireless technologies based on traffic modeling , 2005, 2005 IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications.

[29]  G. K. Chan Effects of sectorization on the spectrum efficiency of cellular radio systems , 1992 .

[30]  W.C.Y. Lee,et al.  Spectrum efficiency in cellular (radio) , 1989 .

[31]  G. Staple,et al.  The end of spectrum scarcity [spectrum allocation and utilization] , 2004, IEEE Spectrum.

[32]  Brian M. Sadler,et al.  Dynamic spectrum access in WLAN channels: empirical model and its stochastic analysis , 2006, TAPAS '06.