Efficient joint power and admission control in underlay cognitive networks using Benders’ decomposition method

2018 ◽  
Vol 129 ◽  
pp. 19-31 ◽  
Author(s):  
Fahime Khoramnejad ◽  
Mehdi Rasti ◽  
Hossein Pedram ◽  
Mehdi Monemi
Keyword(s):  
Admission Control ◽  
Decomposition Method ◽  
Benders Decomposition ◽  
Cognitive Networks ◽  
Joint Power

scholarly journals Optimal and Robust Distributed Resource Allocation for SWIPT-Enabled Cognitive Networks

2020 ◽  
Author(s):  
Yongjun Xu ◽  
Haijian Sun ◽  
Jie Yang ◽  
Guan Gui ◽  
Song Guo
Keyword(s):  
Resource Allocation ◽  
Closed Form ◽  
Power Allocation ◽  
Energy Region ◽  
High Energy ◽  
Cognitive Networks ◽  
Feasible Region ◽  
Power Splitting ◽  
Joint Power ◽  
The Impact

Simultaneous wireless information and power transfer (SWIPT)-enabled cognitive networks (CRNs) is recognized as one of the most promising techniques to improve spectrum efficiency and prolong operation lifetime in 5G and beyond. However, existing methods focus on the centralized algorithm and the power allocation under perfect channel state information (CSI). The analytical solution and the impact of power splitting (PS) on the optimal power allocation strategy are not addressed. In addition, the influence of the PS factor on the feasible region of transit power is rarely analyzed. In this paper, we propose a joint power allocation and PS algorithm under perfect CSI and imperfect CSI, respectively, for multiuser SWIPT-enabled CRNs scenarios. The power minimization of resource allocation problem is formulated as a multivariate nonconvex optimization which is hard to obtain the closed-form solution. Hence, we propose a suboptimal algorithm to alternatively optimize the power allocation and PS coefficient under the cases of the low-harvested energy region and the high-harvested energy region, respectively. Moreover, a closed-form distributed power allocation and PS expressions are derived by the Lagrangian approach. Simulation results confirm the proposed method with good robustness and high energy efficiency.

Latest Advances on Benders Decomposition

2018 ◽  
pp. 5411-5421 ◽  
Author(s):  
Antonios Fragkogios ◽  
Georgios K. D. Saharidis
Keyword(s):  
Mathematical Programming ◽  
Operations Research ◽  
Decomposition Method ◽  
Large Scale ◽  
Benders Decomposition ◽  
Information Science ◽  
Solution Process ◽  
Cpu Time ◽  
Mathematical Problems ◽  
Modern Economic

Operations Research and Mathematical Programming together with Information Science and Technology are tools used to solve various problems in the modern economic environment. This chapter addresses the Benders Decomposition method, which is used for the solution of problems of Operations Research. This method, applied to certain large-scale mathematical problems, can make their solution feasible (if they cannot be solved with another procedure) or can accelerate the solution process in terms of CPU time. The authors provide a thorough presentation of how the decomposition of a problem is made and the Benders algorithm is applied for its solution. Main purpose of this chapter is to analyze the recent studies that address the method's weaknesses and accelerate its application for the faster solution of mathematical problems. A large number of papers is presented and the contribution of each one of them to the improvement of the method is described.

scholarly journals Joint Power and Admission Control Based on Channel Distribution Information: A Novel Two-Timescale Approach

2017 ◽  
Vol 24 (2) ◽  
pp. 196-200 ◽  
Author(s):  
Qitian Chen ◽  
Dong Kang ◽  
Yichu He ◽  
Tsung-Hui Chang ◽  
Ya-Feng Liu
Keyword(s):  
Admission Control ◽  
Joint Power ◽  
Channel Distribution
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