论文标题
部分可观测时空混沌系统的无模型预测
High SNR Analysis of RIS-Aided MIMO Broadcast Channels
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。
We analyze the influence of a reconfigurable intelligent surface (RIS) on the Gram channel eigenvalues in a high signal-to-noise ratio (SNR) scenario. This allows to connect specific channel properties with the rank improvement capabilities of the RIS. In particular, fundamental limits due to a possible line of sight (LOS) setup between the base station (BS) and the RIS are derived. Furthermore, dirty paper coding (DPC) based schemes are compared to linear precoding in such a scenario and it is shown that under certain channel conditions, the performance gap between DPC and linear precoding vanishes.
