论文标题
部分可观测时空混沌系统的无模型预测
UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Recent technological advancements in space, air, and ground components have made possible a new network paradigm called space-air-ground integrated network (SAGIN). Unmanned aerial vehicles (UAVs) play a key role in SAGINs. However, due to UAVs' high dynamics and complexity, real-world deployment of a SAGIN becomes a significant barrier to realizing such SAGINs. UAVs are expected to meet key performance requirements with limited maneuverability and resources with space and terrestrial components. Therefore, employing UAVs in various usage scenarios requires well-designed planning in algorithmic approaches. This paper provides an essential review and analysis of recent learning algorithms in a UAV-assisted SAGIN. We consider possible reward functions and discuss the state-of-the-art algorithms for optimizing the reward functions, including Q-learning, deep Q-learning, multi-armed bandit, particle swarm optimization, and satisfaction-based learning algorithms. Unlike other survey papers, we focus on the methodological perspective of the optimization problem, applicable to various missions on a SAGIN. We consider real-world configurations and the 2-dimensional (2D) and 3-dimensional (3D) UAV trajectories to reflect deployment cases. Our simulations suggest the 3D satisfaction-based learning algorithm outperforms other approaches in most cases. With open challenges discussed at the end, we aim to provide design and deployment guidelines for UAV-assisted SAGINs.
