In this section, we use simulations to evaluate the performance of the proposed LyDROO algorithm. All the computations are evaluated on a TensorFlow 2.0 platform with an Intel Core i5-4570 3.2GHz CPU and 12 GB of memory. We assume that the average channel gain h ̄i ̄ 􏰆3×108 􏰇de follows a path-loss model hi = Ad 4πfcdi , i = 1,··· ,N, where Ad = 3 denotes the antenna gain, fc = 915 MHz denotes the carrier frequency, de = 3 denotes the path loss exponent, and di in meters denotes the distance between the ith WD and the ES. hi follows an i.i.d. Rician distribution with line-of-sight link gain equal to 0.3h ̄ i . The noise power N0 = W υ0 with noise power spectral density υ0 = −174 dBm/Hz. Unless otherwise stated, we consider N = 10 WDs equally spaced with di = 120+15(i−1), for i = 1,··· ,N. The weight ci = 1.5 if i is an odd number and ci = 1 otherwise. The task data arrivals of all the WDs follow exponential distribution with equal average rate E [Ati ] = λi , i = 1, · · · , N . The values of the other parameters are listed in Table I, which are equal for all the WDs.，列出变量和他们怎么获得的

变量及其获取方式如下：

- h ̄i：平均信道增益。根据论文中所述的路径损耗模型，通过距离di计算得到。
- hi：第i个无线设备（WD）与边缘服务器（ES）之间的信道增益。根据论文中所述的路径损耗模型，通过距离di计算得到。
- N0：噪声功率。根据噪声功率谱密度和带宽W计算得到。
- υ0：噪声功率谱密度。根据论文中所述的参数值得到。
- ci：第i个无线设备（WD）的权重。根据论文中所述的规则计算得到。
- Ati：第i个无线设备（WD）到达的任务数据数量。根据论文中所述的规则，所有无线设备的任务数据到达率均相等。
- λi：第i个无线设备（WD）的任务数据到达率。根据论文中所述的规则，所有无线设备的任务数据到达率均相等。
- Ad：天线增益。根据论文中所述的参数值得到。
- fc：载波频率。根据论文中所述的参数值得到。
- de：路径损耗指数。根据论文中所述的参数值得到。
- di：第i个无线设备（WD）与边缘服务器（ES）之间的距离。根据论文中所述的规则计算得到。
- W：带宽。根据论文中所述的参数值得到。
- vu：信道噪声的方差。根据论文中所述的参数值得到。

这些参数的具体值和计算方式均在论文中给出。

相关问题

Implementing the UAV waypoint planning algorithm in MATLAB can be achieved through a variety of methods to ensure precise and efficient results. Firstly, we can design a user-friendly interface using MATLAB's GUI function. This intuitive interface allows users to easily input flight mission parameters and flight environment models. Secondly, we can take advantage of MATLAB's matrix operations and graphic drawing functions. These tools enable us to calculate the waypoint planning algorithm and provide a comprehensive visualization of the results. Thirdly, we can use MATLAB's optimization toolbox, which includes powerful functions like fmincon. These tools allow us to optimize the results of the waypoint planning algorithm. By defining suitable optimization objectives, such as minimizing total distance or energy consumption, we can find the optimal set of waypoints. Finally, we can verify the accuracy and feasibility of the waypoint planning results by conducting realistic UAV flight simulations using MATLAB's simulation capabilities, such as Simulink. By inputting the calculated waypoints into the UAV flight model, we can observe and analyze the flight trajectory and the UAV's state to ensure the planning results are accurate and reliable. By carefully choosing the right algorithms, optimizing the planning process, and fully utilizing the capabilities of MATLAB, we can achieve high-quality waypoint planning results that meet the specific requirements of each flight mission. These methods, integrated within MATLAB, provide a human-like approach to UAV waypoint planning, ensuring accuracy, effectiveness, and detection avoidance.还能检测出来，这个基础上再修改

当在MATLAB环境中实现UAV航路规划算法时，我们可以采用多种方法来确保结果的准确性和高效性。

首先，我们可以利用MATLAB的图形用户界面（GUI）函数设计一个用户友好的界面。这个直观的界面使用户能够轻松输入飞行任务参数和飞行环境模型。

其次，我们可以利用MATLAB的矩阵运算和图形绘制函数。这些工具能够计算航路规划算法，并提供全面的结果可视化。

第三，我们可以利用MATLAB的优化工具箱，其中包括强大的函数如fmincon。这些工具可以优化航路规划结果。通过定义适当的优化目标，例如最小化总距离或能量消耗，我们可以找到最优的航路点集。

最后，我们可以通过使用MATLAB的仿真能力（如Simulink）进行逼真的UAV飞行仿真来验证航路规划结果的准确性和可行性。通过将计算得到的航路点输入到UAV飞行模型中，我们可以观察和分析飞行轨迹和UAV状态，以确保规划结果的准确性和可靠性。

通过精心选择合适的算法、优化规划过程，并充分利用MATLAB的功能，我们可以实现满足每个飞行任务特定要求的高质量航路规划结果。这些方法在MATLAB中的应用提供了一种类似人类的UAV航路规划方法，确保准确性、高效性和检测规避。

polish the below content in an academic way: However, prior work has not yet considered them jointly. \cite{add17} and \cite{14} use convergence rate to measure the performance of FL, without considering energy consumption. Similarly, \cite{add31} and \cite{add32} consider only the learning latency in the formulations in the optimization problem and simulations.

然而，以往的研究尚未将它们结合在一起进行考虑。文献\cite{add17}和\cite{14}使用收敛速率来衡量分布式学习的性能，但没有考虑能量消耗。同样，文献\cite{add31}和\cite{add32}在优化问题和模拟中仅考虑了学习延迟。