AedyVader

AedyVader's Stars

• CSP-GD/deep-learning-summary

  深度學習綱要

  1

• arlosefj/github_interest

  interest repositories

  24247

• vkashyap10/DDPG-LQR

  Language:MATLAB8

• Osmel-dev/sustainable-RF-WET

  Language:MATLAB1

• mahesh191201/UAV-Wireless-Energy-Transfer-using-Machine-Learning

  Language:Python2

• onel2428/WEToverview

  MatLab scripts used to generate the results illustrated in paper: "Massive Wireless Energy Transfer: Enabling Sustainable IoT Towards 6G Era"

  Language:MATLAB123

• msingh017/THE-USE-OF-MOBILE-SINK-FOR-QUALITY-DATA-COLLECTION-IN-ENERGY-HARVESTING-SENSOR-NETWORKS

  Language:MATLAB3

• td2510/UAV_Optimization_Using_KKT_conditions

  Language:MATLAB3

• whalewang410/Energy-Efficient_UAV_Communication_With_Trajectory_Optimization

  Language:MATLAB597

• ABDELHAMED2017/proximity

  Power allocation in a dense cellular network using Q-learning

  Language:Matlab610

• Phuong86/uav-deep-q

  This is the source code using deep Q learning for calculate UAV resource allocation

  Language:MATLAB309

• yyds-xtt/UMC-DGC---CPY

  [1] A Two-Stage Strategy for UAV-enabled Wireless Power Transfer in Unknown Environments 作者：Shi J, Cong P, Zhao L, et al. 出处：IEEE Transactions on Mobile Computing, 2023 摘要：由于具有机动性、高机动性和灵活性等突出优点，无人机(UAV)是可行的移动电源发射器，可以在地理受限的地区快速部署。它们非常适合使用无线电力传输(WPT)技术为能量受限的传感器节点(SN)供电。在本文中，我们研究了一个支持无人机的WPT系统，该系统将功率传输到未知位置的一组SN。一个关键的挑战是如何有效地收集SN的位置并设计功率传输方案。我们制定了一

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