Abstract
Nowadays, 5G puts forward a number of candidate multiple access technologies, among which the non-orthogonal multiple access (NOMA) is attracting more and more attention. Combining cognitive MIMO with NOMA is of great significance to improve the capacity for future mobile communication. Cognitive system includes two kinds of users, which are secondary users (SUs) and primary users (PUs), and the underlay spectrum sharing paradigm needs to consider the interference of the SUs system to the PUs system is below the predetermined threshold. And therefore, in order to reduce interference and improve capacity, we precode firstly at the transmitter. Then SUs were clustered according to the merits of the channel quality and performed power allocation for each cluster. During this process, the mean of the channel matrixs’ trace is used as the dynamic reception weight to enhance the system capacity. Meanwhile, taking the SUs’ quality of service (QoS) and the requirement of successive interference cancelation (SIC) into account. The objective function is NP-hard problem, we need to transform it into system capacity for sub-cluster, and finally using Lagrange function, nonconvex KKT conditions and mathematical induction (MI) to solve the optimal power allocation coefficient, which is between zero and one. The simulation shows that this proposed scheme can improve the capacity obviously compared with the average power allocation.
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Acknowledgements
This work was supported by program for changjiang scholars and innovative research team in university (IRT1299) project of CSTC (cstc2013yykfA40010) and special fund of chongqing key laboratory (CSTC).
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Liao, H. (2018). Capacity Analysis of Secondary User System in Cognitive MIMO Networks Based on NOMA. In: Li, B., Shu, L., Zeng, D. (eds) Communications and Networking. ChinaCom 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 237. Springer, Cham. https://doi.org/10.1007/978-3-319-78139-6_43
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DOI: https://doi.org/10.1007/978-3-319-78139-6_43
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