Cognitive Satellite Spectrum Management Scheme Based on the Cooperative Solidarity Values

With the rapid development of satellite services, the requirement for improving satellite spectrum efficiency has become significant, and attracted great attention from researchers and industry practitioners. Recently, cognitive satellite communication mechanism dynamically accesses idle bands of licensed spectrum while enabling spectrum sharing among satellite and terrestrial agents. In this paper, we propose a new spectrum management scheme for cognitive satellite communications. To effectively improve spectrum efficiency, the concepts of cooperative Solidarity values are adopted by compromising between productivity and solidarity principles. Based on two Solidarity values - Solidarity and Solidarity with size $p$ values, our two-step interactive game approach can leverage the full synergy that gives mutual advantage. The main novelty of our proposed scheme is to ensure the trade-off between marginalism and egalitarianism. Therefore, we can take various benefits in a rational way to reach a fair-efficient agreement under the dynamic changing satellite network environments. Finally, numerical results are provided to confirm the validity of our proposed approach, as well as quantitatively analyze the performance improvement by comparing with the existing protocols.

[1]  W. Marsden I and J , 2012 .

[2]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[3]  Zhenyu Na,et al.  Spectrum Optimization for Cognitive Satellite Communications With Cournot Game Model , 2018, IEEE Access.

[4]  Emilio Calvo,et al.  The Shapley-Solidarity Value for Games with a Coalition Structure , 2013, IGTR.

[5]  Hao Yin,et al.  Joint Transmit Power and Bandwidth Allocation for Cognitive Satellite Network Based on Bargaining Game Theory , 2019, IEEE Access.

[6]  Sungwook Kim,et al.  Game Theory Applications in Network Design , 2014 .

[7]  P. Solal,et al.  A Class of Solidarity Allocation Rules for TU-games , 2015 .

[8]  Sagar Naik,et al.  A new fairness index for radio resource allocation in wireless networks , 2005, IEEE Wireless Communications and Networking Conference, 2005.

[9]  Kang An,et al.  Efficient and Fair Resource Allocation Scheme for Cognitive Satellite-Terrestrial Networks , 2019, IEEE Access.

[10]  Zhenyu Na,et al.  Spectrum Allocation With Asymmetric Monopoly Model for Multibeam-Based Cognitive Satellite Networks , 2018, IEEE Access.

[11]  Symeon Chatzinotas,et al.  Resource Allocation for Cognitive Satellite Communications With Incumbent Terrestrial Networks , 2015, IEEE Transactions on Cognitive Communications and Networking.

[12]  Sungwook Kim Trust-Based Bargaining Game Model for Cognitive Radio Spectrum Sharing Scheme , 2012, IEICE Trans. Commun..

[13]  N. Dagan,et al.  The bankruptcy problem: a cooperative bargaining approach , 1993 .

[14]  Symeon Chatzinotas,et al.  An Uplink UE Group-Based Scheduling Technique for 5G mMTC Systems Over LEO Satellite , 2019, IEEE Access.

[15]  Xiqi Gao,et al.  Near Optimal Timing and Frequency Offset Estimation for 5G Integrated LEO Satellite Communication System , 2019, IEEE Access.

[16]  François Gagnon,et al.  Coalitional Games for Joint Co-Tier and Cross-Tier Cooperative Spectrum Sharing in Dense Heterogeneous Networks , 2016, IEEE Access.

[17]  Yan Zhang,et al.  Energy efficient hybrid satellite terrestrial 5G networks with software defined features , 2017, Journal of Communications and Networks.

[18]  Heejung Yu,et al.  5G Ultra-Reliable Low-Latency Communication Implementation Challenges and Operational Issues with IoT Devices , 2019, Electronics.

[19]  Shun Zhang,et al.  Load Balancing Based on Cache Resource Allocation in Satellite Networks , 2019, IEEE Access.