Pilot-Free Optimal Control over Wireless Networks: A Control-Aided Channel Prediction Approach

TL;DR

This paper introduces a pilot-free control framework for wireless networked control systems that uses control-aided channel prediction with Kalman filters and deep learning, improving control and prediction accuracy without needing real-time CSI.

Contribution

It proposes a novel pilot-free control method combining Kalman filter-based channel prediction and optimal control derived from the Bellman principle, with extensions to nonlinear plants using deep learning.

Findings

Outperforms benchmark schemes in control performance.

Achieves higher channel prediction accuracy.

Provides stability guarantees for the control system.

Abstract

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.