TY - JOUR
T1 - Cooperative spectrum prediction in multi-PU multi-SU cognitive radio networks
AU - Xing, Xiaoshuang
AU - Jing, Tao
AU - Cheng, Wei
AU - Huo, Yan
AU - Cheng, Xiuzhen
AU - Znati, Taieb
N1 - Funding Information:
Acknowledgments The authors would like to thank the support from the National Natural Science Foundation of China (Grant No. 61371069, 61272505, and 61172074), and the National Science Foundation of the US (CNS-1162057 and CNS-1265311).
PY - 2014/8
Y1 - 2014/8
N2 - Spectrum sensing is considered as the cornerstone of cognitive radio networks (CRNs). However, sensing the wide-band spectrum results in delays and resource wasting. Spectrum prediction, also known as channel status prediction, has been proposed as a promising approach to overcome these shortcomings. Prediction of the channel occupancy, when feasible, provides adequate means for an SU to determine, with a high probability, when to evacuate a channel it currently occupies in anticipation of the PU's return. Spectrum prediction has great potential to reduce interference with PU activities and significantly enhance spectral efficiency. In this paper, we propose a novel, coalitional game theory based approach to investigate cooperative spectrum prediction in multi-PU multi-SU CRNs. In this approach, cooperative groups, also referred to as coalitions, are formed through a proposed coalition formation algorithm. The novelty of this work, in comparison to existing cooperative sensing approaches, stems from its focus on the more challenging case of multi-PU CRNs and the use of an efficient coalition formation algorithm, centered on the concept of core, to ensure stability. Theoretical analysis is conducted on the upper bound of the coalition size and the stability of the formed coalition structure. A through simulation study is performed to assess the effectiveness of the proposed approach. The simulation results indicate that cooperative spectrum prediction leads to more accurate prediction decisions, in comparison with local spectrum prediction individually performed by SUs. To the best of our knowledge, this work is the first to use coalitional game theory to study cooperative spectrum prediction in CRNs, involving multiple PUs.
AB - Spectrum sensing is considered as the cornerstone of cognitive radio networks (CRNs). However, sensing the wide-band spectrum results in delays and resource wasting. Spectrum prediction, also known as channel status prediction, has been proposed as a promising approach to overcome these shortcomings. Prediction of the channel occupancy, when feasible, provides adequate means for an SU to determine, with a high probability, when to evacuate a channel it currently occupies in anticipation of the PU's return. Spectrum prediction has great potential to reduce interference with PU activities and significantly enhance spectral efficiency. In this paper, we propose a novel, coalitional game theory based approach to investigate cooperative spectrum prediction in multi-PU multi-SU CRNs. In this approach, cooperative groups, also referred to as coalitions, are formed through a proposed coalition formation algorithm. The novelty of this work, in comparison to existing cooperative sensing approaches, stems from its focus on the more challenging case of multi-PU CRNs and the use of an efficient coalition formation algorithm, centered on the concept of core, to ensure stability. Theoretical analysis is conducted on the upper bound of the coalition size and the stability of the formed coalition structure. A through simulation study is performed to assess the effectiveness of the proposed approach. The simulation results indicate that cooperative spectrum prediction leads to more accurate prediction decisions, in comparison with local spectrum prediction individually performed by SUs. To the best of our knowledge, this work is the first to use coalitional game theory to study cooperative spectrum prediction in CRNs, involving multiple PUs.
KW - Coalition formation
KW - Coalitional game theory
KW - Cognitive radio networks
KW - Cooperative spectrum prediction
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U2 - 10.1007/s11036-014-0507-x
DO - 10.1007/s11036-014-0507-x
M3 - Article
AN - SCOPUS:84907599236
SN - 1383-469X
VL - 19
SP - 502
EP - 511
JO - Mobile Networks and Applications
JF - Mobile Networks and Applications
IS - 4
ER -