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dc.contributor.advisorMancuso, Vincenzo 
dc.contributor.authorAsadi, Arash 
dc.date.accessioned2021-07-13T09:56:37Z
dc.date.available2021-07-13T09:56:37Z
dc.date.issued2012-10-03
dc.identifier.citation1] K. Akkarajitsakul, E. Hossain, and D. Niyato. Cooperative packet delivery in hybrid wireless mobile networks: A coalitional game approach. IEEE Transactions on Mobile Computing, (99):1–1, 2012. [2] M. Andrews, K. Kumaran, K. Ramanan, A. Stolyar, R. Vijayakumar, and P. Whiting. Scheduling in a queuing system with asynchronously varying service rates. Probability in the Engineering and Informational Sciences, 18:191–217, April 2004. [3] K. Apt and T. Radzik. Stable partitions in coalitional games. Arxiv preprint cs/0605132, 2006. [4] K. Apt and S. Witzel. A generic approach to coalition formation. 2006. [5] S. Bandyopadhyay and E. Coyle. An energy efficient hierarchical clustering algorithm for wireless sensor networks. In INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications. IEEE Societies, volume 3, pages 1713–1723. IEEE, 2003. [6] W. Choi and J. Andrews. The capacity gain from base station cooperative scheduling in a MIMO DPC cellular system. In IEEE International Symposium on Information Theory, pages 1224 –1228, July 2006. [7] M. Dohler et al. Virtual antenna arrays. PhD thesis, University of London, 2004. [8] E. Hahne. Round-robin scheduling for max-min fairness in data networks. IEEE Journal on Selected Areas in Communications, 9(7):1024–1039, 1991. [9] W. Heinzelman, A. Chandrakasan, and H. Balakrishnan. Energy-efficient communication protocol for wireless microsensor networks. In System Sciences, 2000. Proceedings of the 33rd Annual Hawaii International Conference on, pages 10–pp. IEEE, 2000. [10] K. Khalil, M. Karaca, O. Ercetin, and E. Ekici. Optimal scheduling in cooperate-to-join cognitive radio networks. In Proceedings of IEEE INFOCOM, pages 3002 –3010, April 2011. [11] R. Knopp and P. Humblet. Information capacity and power control in single-cell multiuser communications. In Proceedings of IEEE ICC, volume 1, pages 331 –335 vol.1, June 1995. [12] L.LeandE.Hossain.Multihopcellularnetworks:Potentialgains,researchchallenges, and a resource allocation framework. Communications Magazine, IEEE, 45(9):66–73, 2007. [13] J.-W. Lee, R. Mazumdar, and N. Shroff. Opportunistic power scheduling for multiserver wireless systems with minimum performance constraints. In Proceedings of IEEE INFOCOM, volume 2, pages 1067 – 1077 vol.2, March 2004. [14] S. Lee, K. Kim, K. Hong, D. Griffith, Y. Kim, and N. Golmie. A probabilistic call admission control algorithm for WLAN in heterogeneous wireless environment. IEEE Transactions on Wireless Communications, 8(4):1672–1676, 2009. [15] C. Lin and M. Gerla. Adaptive clustering for mobile wireless networks. IEEE Journal on Selected Areas in Communications, 15(7):1265–1275, 1997. [16] E. Liu and K. Leung. Proportional fair scheduling: analytical insight under rayleigh fading environment. In Proceedings of IEEE WCNC, pages 1883–1888. IEEE, 2008. [17] D. Ray. A game-theoretic perspective on coalition formation. OUP Oxford, 2007. [18] W. Saad, Z. Han, M. Debbah, and A. Hjorungnes. A distributed merge and split algorithm for fair cooperation in wireless networks. In Proceedings of IEEE ICC Work- shops, pages 311–315. Ieee, 2008. [19] W. Saad, Z. Han, M. Debbah, A. Hjorungnes, and T. Basar. Coalitional game theory for communication networks. Signal Processing Magazine, IEEE, 26(5):77–97, 2009. [20] W. Saad, Z. Han, M. Debbah, A. Hjorungnes, and T. Basar. Coalitional games for distributed collaborative spectrum sensing in cognitive radio networks. In INFOCOM 2009, IEEE, pages 2114–2122. IEEE, 2009. [21] A. Sendonaris, E. Erkip, and B. Aazhang. User cooperation diversity. part i. system description. Communications, IEEE Transactions on, 51(11):1927–1938, 2003. [22] M. Sereno. Cooperative game theory framework for energy efficient policies in wireless networks. pages 1 –9, May 2012. [23] S. Sesia, I. Toufik, and M. Baker. LTE-the UMTS long term evolution: from theory to practice. Wiley, 2011. [24] N.SharmaandL.Ozarow.A study of opportunism for multiple-antenna systems.IEEE Transactions on Information Theory, 51(5):1804 – 1814, May 2005. [25] W.-F. A. Specification. Wi-Fi Peer-to-Peer (P2P) Specification v1.1, 2011. [26] Third Generation Partnership Project (3GPP). Physical layer procedures (Release 10) for Evolved Universal Terrestrial Radio Access (E-UTRA). 3GPP TR 36.213 v 10.5.0, March 2012. [27] P.Viswanath,D.Tse,andR.Laroia. Opportunistic beamforming using dumb antennas. In IEEE Transactions on Information Theory, page 449, June 2002. [28] D. Wu, Y. Cai, L. Zhou, and J. Wang. A cooperative communication scheme based on coalition formation game in clustered wireless sensor networks. IEEE Transactions on Wireless Communications, (99):1–11, March 2012. [29] H.Wu,C.Qiao,S.De,andO.Tonguz. Integrated cellular and adhoc relaying systems: iCAR. Selected Areas in Communications, IEEE Journal on, 19(10):2105–2115, 2001. [30] J. Yang, Z. Yifan, W. Ying, and Z. Ping. Average rate updating mechanism in pro- portional fair scheduler for hdr. In Global Telecommunications Conference, 2004. GLOBECOM’04. IEEE, volume 6, pages 3464–3466. IEEE, 2004.
dc.identifier.urihttp://hdl.handle.net/20.500.12761/1098
dc.description“Premio Extraordinario de Máster Oficial Universidad Carlos III de Madrid – Curso 2011/2012. Programa de Máster Interuniversitario en Ingeniería Telemática.” Masters thesis defended on October 3rd, 2012, in Madrid, Spain. The prize was awarded on December 18th, 2012.
dc.description.abstractOpportunistic scheduling was initially proposed to exploit user channel diversity for network capacity enhancement. However, the achievable gain of opportunistic schedulers is generally restrained due to fairness considerations which impose a tradeoff between fairness and throughput. In this dissertation, we show via analysis and simulation that opportunistic scheduling not only increases network throughput dramatically, but also can be fair to the users when they cooperate, in particular by forming clusters. We propose to leverage smartphone’s dual-radio interface capabilities to form clusters among mobile users, and we design simple and scalable cluster-based opportunistic scheduling strategies which would incentivize mobile users to form clusters. We use a coalitional game theory approach to analyze the cluster formation mechanism, and show that proportional fair-based intra-cluster payoff distribution would bring significant incentive to all mobile users regardless of their channel quality.
dc.language.isoeng
dc.subject.lccQ Science::Q Science (General)
dc.subject.lccQ Science::QA Mathematics::QA75 Electronic computers. Computer science
dc.subject.lccT Technology::T Technology (General)
dc.subject.lccT Technology::TA Engineering (General). Civil engineering (General)
dc.subject.lccT Technology::TK Electrical engineering. Electronics Nuclear engineering
dc.titleOpportunistic cellular communications with clusters of dual-radio mobilesen
dc.typemaster thesis
dc.type.hasVersionVoR
dc.rights.accessRightsopen access
dc.description.departmentTelematics Engineering
dc.description.institutionUniversidad Carlos III de Madrid, Spain
dc.page.total38
dc.description.statuspub
dc.eprint.idhttp://eprints.networks.imdea.org/id/eprint/374


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