ELCE Department, Ryerson University, Toronto, ON, Canada.
Sensors (Basel). 2013 Aug 21;13(8):11032-50. doi: 10.3390/s130811032.
Designing energy-efficient cognitive radio sensor networks is important to intelligently use battery energy and to maximize the sensor network life. In this paper, the problem of determining the power allocation that maximizes the energy-efficiency of cognitive radio-based wireless sensor networks is formed as a constrained optimization problem, where the objective function is the ratio of network throughput and the network power. The proposed constrained optimization problem belongs to a class of nonlinear fractional programming problems. Charnes-Cooper Transformation is used to transform the nonlinear fractional problem into an equivalent concave optimization problem. The structure of the power allocation policy for the transformed concave problem is found to be of a water-filling type. The problem is also transformed into a parametric form for which a ε-optimal iterative solution exists. The convergence of the iterative algorithms is proven, and numerical solutions are presented. The iterative solutions are compared with the optimal solution obtained from the transformed concave problem, and the effects of different system parameters (interference threshold level, the number of primary users and secondary sensor nodes) on the performance of the proposed algorithms are investigated.
设计节能的认知无线电传感器网络对于智能地利用电池能量和最大化传感器网络寿命非常重要。在本文中，将最大化基于认知无线电的无线传感器网络的能量效率的功率分配问题表示为一个约束优化问题，其中目标函数是网络吞吐量与网络功率的比值。所提出的约束优化问题属于一类非线性分数规划问题。使用 Charnes-Cooper 变换将非线性分数问题转换为等效的凹优化问题。发现转换后的凹问题的功率分配策略的结构为注水类型。该问题也被转换为具有 ε-最优迭代解的参数形式。证明了迭代算法的收敛性，并给出了数值解。将迭代解与从转换后的凹问题获得的最优解进行比较，并研究了不同系统参数（干扰门限电平、主用户和次传感器节点的数量）对所提出算法性能的影响。
