A Simulation of Electromagnetic Compatibility QoS Model in Cognitive Radio Network

Cognitive radio system is a typical cognitive system. Spectrum decision in cognitive radio must consider the QoS requirement. The requirements in this paper are to ensure the largest network and the optimal parameter for communication at the same time. To satisfy these requirements, spectrum decision needs to select appropriate communication parameters, adjust subnet topology, and take electromagnetic compatibility (EMC) into account for multi-transceiver users. This paper analyzes the principle of electromagnetic compatibility and gives the flow of electromagnetic compatibility analysis (EMCA). Then, a precise QoS model based on the EMCA is proposed. The precise EMC QoS (pEMC) model takes the topological bottlenecks, electromagnetic compatibility, and spectrum pool capacity into account. The complexity of EMCA will rapidly increase with the spread of spectrum pool of topology, and a real-time spectrum decision cannot be guaranteed. Then, an approximate EMC QoS (aEMC) model is proposed and the consistency of these two models is simulated. The results show a high consistency between the pEMC and aEMC model so that the aEMC model can substitute for the pEMC model in practice.

[1]  Xiao-Xin He,et al.  Optimal Algorithm for Cognitive Spectrum Decision Making , 2012 .

[2]  S. A. Hanna Intermodulation and harmonic analysis for land mobile radio communications , 1998, VTC '98. 48th IEEE Vehicular Technology Conference. Pathway to Global Wireless Revolution (Cat. No.98CH36151).

[3]  Joseph Mitola,et al.  Cognitive Radio An Integrated Agent Architecture for Software Defined Radio , 2000 .

[4]  J. H. McMahon Interference and propagation formulas and tables used in the federal communications commission spectrum management task force land mobile frequency assignment model , 1974 .

[5]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[6]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[7]  Zhou Xin,et al.  Efficient algorithm for extreme maximal biclique mining in cognitive frequency decision making , 2011, 2011 IEEE 3rd International Conference on Communication Software and Networks.

[8]  Deng Yong,et al.  Extreme Maximal Weighted Frequent Itemset Mining for Cognitive Frequency Decision Making , 2011, Proceedings of 2011 International Conference on Computer Science and Network Technology.