UAV协助下非正交多址接入使能的数据采集系统中能效优化机制

Energy efficiency optimization mechanism for UAV-assisted and non-orthogonal multiple access-enabled data collection system

无人机(UAV)协助下非正交多址接入(NOMA)使能的数据采集系统,考虑了地空概率信道模型和服务质量约束,并联合优化UAV三维布局设计和传感器功率分配最大化所有传感器的总能效。针对原混合整数非凸规划问题,提出了一种基于凸优化理论、深度学习理论和哈里斯鹰优化(HHO)算法的能效优化机制。在任意给定的UAV三维布局下,首先将功率分配子问题等价转化为凸优化问题;其次基于最优的功率分配方案,采用深度神经网络(DNN)构建从传感器位置到UAV三维布局的映射,并利用HHO算法离线训练最佳映射对应的模型参数。训练后的机制仅需执行少量代数运算并求解单个凸优化问题。仿真实验结果表明,在传感器数为12的情况下,相较于基于粒子群算法的遍历搜索机制,所提机制在仅损失约4.73%的总能效的情况下将运算时间降低了5个数量级。

In the Unmanned Aerial Vehicle(UAV)-assisted and Non-Orthogonal Multiple Access(NOMA)-enabled data collection system,the total energy efficiency of all sensors is maximized by jointly optimizing the three-dimensional placement design of the UAVs and the power allocation of sensors under the ground-air probabilistic channel model and the quality-of-service requirements.To solve the original mixed-integer non-convex programming problem,an energy efficiency optimization mechanism was proposed based on convex optimization theory,deep learning theory and Harris Hawk Optimization(HHO)algorithm.Under any given three-dimensional placement of the UAVs,first,the power allocation subproblem was equivalently transformed into a convex optimization problem.Then,based on the optimal power allocation strategy,the Deep Neural Network(DNN)was applied to construct the mapping from the positions of the sensors to the threedimensional placement of the UAVs,and the HHO algorithm was further utilized to train the model parameters corresponding to the optimal mapping offline.The trained mechanism only involved several algebraic operations and needed to solve a single convex optimization problem.Simulation experimental results show that compared with the travesal search mechanism based on particle swarm optimization algorithm,the proposed mechanism reduces the average operation time by 5 orders of magnitude while sacrificing only about 4.73%total energy efficiency in the case of 12 sensors.