Abstract
The majority of existing studies on power control in cognitive radio networks focus on maximization of signal-to-interference-noise ratio (SINR), while this paper firstly introduces the signal-to-leakage-and-noise ratio (SLNR)-oriented power control to optimize throughput in a cognitive radio network (CRN), where massive secondary connections (SCs) and a primary user (PU) coexist with each other sharing the same frequency spectrum. Considering the practical challenge that the channel gains between SCs and PU are typically uncertain, we introduce a probabilistic interference constraint to protect the PU’s transmission and reformulate it according to the Lyapunov’s central limit theorem (CLT). Then, we apply the convex optimization theory to solve the intractable problem by excluding the probabilistic constraint. Especially, a novel algorithm based on the first-order Lagrangian is developed where the dual variables are updated simultaneously. Furthermore, we provide numerial results using different parameter, which display that the proposed method can achieve higher throughput with much lower computational complexity comparing with the existing literature.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China (No. 61501510 and No. 61301160), and Natural Science Foundation of Jiangsu Province (No. BK20150717), China Postdoctoral Science Foundation Funded Project, and Jiangsu Planned Projects for Postdoctoral Research Funds.
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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wang, L. et al. (2017). SLNR-Oriented Power Control in Cognitive Radio Networks with Channel Uncertainty. In: Xin-lin, H. (eds) Machine Learning and Intelligent Communications. MLICOM 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 183. Springer, Cham. https://doi.org/10.1007/978-3-319-52730-7_37
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DOI: https://doi.org/10.1007/978-3-319-52730-7_37
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