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首页> 外文期刊>IEEE Transactions on Vehicular Technology >Learning-Based Joint Optimization of Transmit Power and Harvesting Time in Wireless-Powered Networks With Co-Channel Interference
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Learning-Based Joint Optimization of Transmit Power and Harvesting Time in Wireless-Powered Networks With Co-Channel Interference

机译:具有同频道干扰的无线供电网络中基于学习的联合优化发射功率和收获时间

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摘要

In this paper, we consider a wireless-powered network with co-channel interference where the transmitters control their transmit power and receivers harvest wireless energy using a time switching policy. Considering the interference channels among multiple nodes, we jointly optimize the transmit power and energy harvesting time to maximize the energy efficiency of the network. To solve this non-convex optimization problem, we first design an iterative algorithm based on a typical optimization technique, and then, propose a learning algorithm based on a neural network with a proper loss function. Simulation results show that the proposed learning algorithm can achieve a near-optimal energy efficiency with reducing the computational complexity, compared to an iterative algorithm with a suboptimal performance.
机译:在本文中,我们考虑了具有同频道干扰的无线网络,其中发射机控制其发射功率,而接收机则使用时间切换策略来收集无线能量。考虑到多个节点之间的干扰通道,我们共同优化了发射功率和能量收集时间,以最大化网络的能量效率。为了解决这一非凸优化问题,我们首先设计了一种基于典型优化技术的迭代算法,然后提出了一种具有适当损失函数的基于神经网络的学习算法。仿真结果表明,与具有次优性能的迭代算法相比,所提出的学习算法可以在降低计算复杂度的同时实现接近最优的能量效率。

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