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dc.contributor.authorPaul, Ayan
dc.contributor.authorMaitra, Madhubanti
dc.contributor.authorMandal, Swarup
dc.contributor.authorSadhukhan, Samir K.
dc.contributor.authorSaha, Debashish
dc.descriptionPaul, Ayan, BSNL, 547B, Garia Garden Marvel Castle, Kolkata 700084, India; Maitra, Madhubanti, Electrical Engineering Department, Jadavpur University, India; Mandal, Swarup, Wipro Technologies Limited, Kolkata, India; Sadhukhan, Samir K., MIS Group, Indian Institute of Management, Calcutta, Kolkata, India; Saha, Debashish, MIS Group, Indian Institute of Management, Calcutta, Kolkata, India
dc.descriptionISSN/ISBN - 09521976
dc.descriptionDOI - 10.1016/j.engappai.2013.06.008
dc.description.abstractThis work proposes a dynamic and fair spectrum management strategy to be adopted by a service provider (SP). Here, we consider a scenario where multiple access networks (ANs) of different (not necessarily competing) technologies are owned by a single SP. We envisage that an SP employs an entity, called local spectrum controller (LSC), which manages a common pool of spectrum and is responsible for distributing the spectrum to individual ANs in a fair manner. Since the available spectrum is inadequate to satisfy the aggregate demand from all ANs simultaneously, LSC has to employ a dynamic spectrum allocation strategy. We have modeled the problem as an n-player cooperative bargaining game and have solved the problem with the help of solution techniques namely, Nash bargaining solution (NBS), Kalai-Smorodinsky bargaining solution (KSBS) and modified Thomson bargaining solution (MTBS). We have presented two novel heuristics to compute KSBS and MTBS. Moreover, a suitable utility function for the AN with respect to its received spectrum have been presented. We have also identified possible objectives of LSC (i.e.; SP), namely minimizing overall dissatisfaction (MOD) of heterogeneous network, maintaining equality of distribution (MED) and maximizing proportional fairness (MPF). Finally, we have compared performances of above solutions with max-min fairness solution and Shapley value solution with respect to the above objectives of LSC. � 2013 Elsevier Ltd.
dc.publisherEngineering Applications of Artificial Intelligence
dc.subjectCooperative bargaining game
dc.subjectDynamic spectrum allocation
dc.subjectKalai-Smorodinsky bargaining solution
dc.subjectModified Thomson bargaining solution
dc.subjectNash bargaining solution
dc.titleA dynamic spectrum management strategy based on cooperative bargaining framework
Appears in Collections:Management Information Systems

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