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Energy-efficient power allocation for simultaneous wireless information-and-energy multicast in cognitive OFDM systems

机译:认知OFDM系统中同时进行无线信息和能量多播的高能效功率分配

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In this paper, we investigate power allocation for simultaneous wireless information-and-energy multicast in cognitive OFDM systems. Our objective is to maximize the energy efficiency (EE) subject to the maximum power constraint at cognitive base station (CBS), maximum receiver interference constraint at each primary user (PU) and minimum harvested energy constraint at each energy receiver (ER). Due to the non-convexity of objective function, fractional programming is adopted to transform the nonconvex problem to a convex one. However, the complexity of the traditional optimization method, i.e., interior point method, is still too high to solve the transformed problem. To this end, a bisection-search-based suboptimal algorithm is proposed. Simulation results show that the proposed algorithm can greatly reduce the complexity (up to 1/12 at most) at the cost of tiny performance loss (less than 2%) compared with traditional convex optimization algorithms.
机译:在本文中,我们研究了认知OFDM系统中同时进行无线信息和能量多播的功率分配。我们的目标是在认知基站(CBS)的最大功率约束,每个主要用户(PU)的最大接收器干扰约束和每个能量接收器(ER)的最小采集能量约束的情况下,最大化能效(EE)。由于目标函数的非凸性,采用分数规划将非凸问题转化为凸问题。但是,传统优化方法即内点法的复杂度仍然太高而不能解决变换后的问题。为此,提出了一种基于对分搜索的次优算法。仿真结果表明,与传统的凸优化算法相比,所提出的算法可以以最小的性能损失(小于2%)为代价,极大地降低复杂度(最多不超过1/12)。

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