Research Article
Dual Optimal Robust Power Control Algorithm Based on Channel Uncertainty
@INPROCEEDINGS{10.1007/978-3-030-21730-3_4, author={Guanglong Yang and Xuezhi Tan and Xiao Wang}, title={Dual Optimal Robust Power Control Algorithm Based on Channel Uncertainty}, proceedings={Green Energy and Networking. 6th EAI International Conference, GreeNets 2019, Dalian, China, May 4, 2019, Proceedings}, proceedings_a={GREENETS}, year={2019}, month={6}, keywords={Cognitive radio Underlay spectrum sharing Distributed power control}, doi={10.1007/978-3-030-21730-3_4} }
- Guanglong Yang
Xuezhi Tan
Xiao Wang
Year: 2019
Dual Optimal Robust Power Control Algorithm Based on Channel Uncertainty
GREENETS
Springer
DOI: 10.1007/978-3-030-21730-3_4
Abstract
In order to improve the fault-tolerant ability under parameter perturbation, the robust power control problem based on channel uncertainty is studied. In this paper, the robust optimization theory and stochastic optimization theory commonly used to deal with uncertain parameters are deeply analyzed, and the mathematical significance, application scenarios, advantages and disadvantages of the two optimization theories are summarized. A comprehensive solution for bounded uncertainty and probabilistic constraints is proposed. On the one hand, the scheme guarantees the rights and interests of the primary user under the worst error, on the other hand, the secondary user is satisfied with a certain interrupt probability under the condition of system robustness. In this paper, the main user interference temperature and the secondary user probability SINR are taken as the constraint conditions, the maximum throughput of the system is transformed into a convex optimization form, and the Lagrange dual (LD, Lagrange Duality) principle is used to solve the problem. The results show that the double optimization solution is a compromise between probabilistic constraint algorithm, Worst-case algorithm and non-robust algorithm, which not only fully protects the rights and interests of the primary user, but also takes into account the robustness. At the same time, the conservatism of the bounded uncertain design method is avoided.