随着无人机作为空中中继和终端技术的迅速发展,物理层安全问题在近几年间已经成为一个研究热点.本文将无人机发射的通信功率分为两部分(保密信息功率和人工噪声功率),在传输保密信息时能有效防止非法窃听,同时考虑了无人机在空中飞行时...随着无人机作为空中中继和终端技术的迅速发展,物理层安全问题在近几年间已经成为一个研究热点.本文将无人机发射的通信功率分为两部分(保密信息功率和人工噪声功率),在传输保密信息时能有效防止非法窃听,同时考虑了无人机在空中飞行时所需的动力功耗,通过联合优化无人机的飞行轨迹和功率分配比,以实现在固定能量下平均传输信息量最大化的目的.将这一情景建模成马尔可夫模型(MDP),并利用Curiosity-Driven Deep Q-learning Network(C-DQN)算法进行训练优化,结果表明,该算法具有良好的收敛效果.展开更多
This paper performs an experimental study for inverse load reconstruction. By measuring and analyzing the load characteristics of different home and office electric devices, the author shows that a reconstruction of t...This paper performs an experimental study for inverse load reconstruction. By measuring and analyzing the load characteristics of different home and office electric devices, the author shows that a reconstruction of the individual power consumption of different loads from the total measurement of a single power meter is possible.展开更多
文摘随着无人机作为空中中继和终端技术的迅速发展,物理层安全问题在近几年间已经成为一个研究热点.本文将无人机发射的通信功率分为两部分(保密信息功率和人工噪声功率),在传输保密信息时能有效防止非法窃听,同时考虑了无人机在空中飞行时所需的动力功耗,通过联合优化无人机的飞行轨迹和功率分配比,以实现在固定能量下平均传输信息量最大化的目的.将这一情景建模成马尔可夫模型(MDP),并利用Curiosity-Driven Deep Q-learning Network(C-DQN)算法进行训练优化,结果表明,该算法具有良好的收敛效果.
文摘This paper performs an experimental study for inverse load reconstruction. By measuring and analyzing the load characteristics of different home and office electric devices, the author shows that a reconstruction of the individual power consumption of different loads from the total measurement of a single power meter is possible.
