无导频最优无线网络控制：一种基于控制的信道预测方法
Pilot-Free Optimal Control over Wireless Networks: A Control-Aided Channel Prediction Approach
AI总结：
本文提出一种无导频的无线网络最优控制方法，结合基于控制的信道预测，通过卡尔曼滤波和随机近似算法实现高效控制策略，适用于线性和非线性系统。
摘要：
在无线网络化控制系统（WNCS）的最优控制器设计中，依赖实时信道状态信息（CSI）是一个反复出现的主题。然而，由于无线信道的时间变化性，事先获取准确的CSI历来极具挑战性。在本文中，我们提出了一种无导频的框架，用于无线信道的最优控制，其中控制命令由植物状态和基于控制的信道预测共同生成。对于在正交频分复用（OFDM）架构上运行的线性植物，通过卡尔曼滤波器（KF）进行信道预测，最优控制策略则基于贝尔曼原理推导。为了解决计算最优控制策略时的维度灾难问题，我们使用耦合代数黎曼方程（CARE）来近似解，该解可通过随机近似（SA）算法高效计算。通过证明在所得到的控制策略下，信道预测器和闭环系统的稳定性，建立了充分条件，以保证稳定近似CARE解的存在性和唯一性，并建立了基于SA的控制算法的收敛性。该框架进一步通过结合基于KalmanNet的预测器和马尔可夫调节深度确定性策略梯度（MM-DDPG）控制器，扩展到非线性植物的一般无线架构中。数值结果表明，所提出的无导频方法在线性及非线性场景中均在控制性能和信道预测精度上优于基准方案。
英文摘要：
A recurring theme in optimal controller design for wireless networked control systems (WNCS) is the reliance on real-time channel state information (CSI). However, acquiring accurate CSI a priori is notoriously challenging due to the time-varying nature of wireless channels. In this work, we propose a pilot-free framework for optimal control over wireless channels in which control commands are generated from plant states together with control-aided channel prediction. For linear plants operating over an orthogonal frequency-division multiplexing (OFDM) architecture, channel prediction is performed via a Kalman filter (KF), and the optimal control policy is derived from the Bellman principle. To alleviate the curse of dimensionality in computing the optimal control policy, we approximate the solution using a coupled algebraic Riccati equation (CARE), which can be computed efficiently via a stochastic approximation (SA) algorithm. Rigorous performance guarantees are established by proving the stability of both the channel predictor and the closed-loop system under the resulting control policy, providing sufficient conditions for the existence and uniqueness of a stabilizing approximate CARE solution, and establishing convergence of the SA-based control algorithm. The framework is further extended to nonlinear plants under general wireless architectures by combining a KalmanNet-based predictor with a Markov-modulated deep deterministic policy gradient (MM-DDPG) controller. Numerical results show that the proposed pilot-free approach outperforms benchmark schemes in both control performance and channel prediction accuracy for linear and nonlinear scenarios.
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