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Title: Spectrum allocation with asymmetric monopoly model for multibeam-based cognitive satellite networks
Authors: Li, Feng
Liu, Xin
Lam, Kwok-Yan
Na, Zhenyu
Hua, Jingyu
Wang, Jian
Wang, Li
Keywords: Spectrum Allocation
Cognitive Satellite Communications
Issue Date: 2018
Source: Li, F., Liu, X., Lam, K. Y., Na, Z., Hua, J., Wang, J., et al. (2018). Spectrum allocation with asymmetric monopoly model for multibeam-based cognitive satellite networks. IEEE Access, 6, 9713-9722.
Series/Report no.: IEEE Access
Abstract: With the rapid development of modern satellite communications, broadband satellite services are experiencing a period of remarkable growth in both user population and available bandwidth. Efficient spectrum management is required in order to meet the ever-increasing demand for broadband spectrum. In this paper, a spectrum allocation scheme for cognitive satellite networks is proposed to improve spectrum efficiency by addressing the situation that scarce spectrum resource is under-utilized while the overall demands of cognitive satellite users are not satisfied. The proposed scheme is devised to optimize bandwidth efficiency by achieving the Bayesian equilibrium through spectrum competition among cognitive satellite users. Due to the scarcity of spectrum resource, satellite systems have to eliminate some of the cognitive users' transmission information types in order to fulfill the spectrum needs of priority users. After declining one or several kinds of modulation modes, cognitive users can update their spectrum lists. With the increase in number of cognitive users and their corresponding transmission information types, the complexity of the optimization problem increases significantly and becomes a computation burden to cognitive satellite systems. Therefore, in this paper, we propose a simplified solution for spectrum resource optimization in a computation-efficient manner. Through rounds of eliminating operations, the satellite systems can identify the final Bayesian equilibrium as an optimal spectrum allocation strategy. We also provide proofs for the existence of Bayesian equilibrium. Numerical results are given to evaluate the performance of our proposed method from diverse perspectives.
DOI: 10.1109/ACCESS.2018.2797278
Rights: © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See for more information.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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