Real-Time Remote Estimation with Hybrid ARQ in Wireless Networked Control

TL;DR

This paper introduces a hybrid ARQ-based framework for real-time remote estimation of LTI systems over wireless channels, optimizing transmission control to balance reliability and freshness, and demonstrating significant performance improvements.

Contribution

It formulates a new optimization problem for sensor transmission control in HARQ systems, deriving conditions for stability and proposing a low-complexity suboptimal policy.

Findings

Optimal policy stabilizes remote estimation and minimizes MSE.

Proposed policy outperforms conventional non-HARQ methods.

Derived conditions ensure bounded long-term estimation error.

Abstract

Real-time remote estimation is critical for mission-critical applications including industrial automation, smart grid and tactile Internet. In this paper, we propose a hybrid automatic repeat request (HARQ)-based real-time remote estimation framework for linear time-invariant (LTI) dynamic systems. Considering the estimation quality of such a system, there is a fundamental tradeoff between the reliability and freshness of the sensor's measurement transmission. We formulate a new problem to optimize the sensor's online transmission control policy for static and Markov fading channels, which depends on both the current estimation quality of the remote estimator and the current number of retransmissions of the sensor, so as to minimize the long-term remote estimation mean squared error (MSE). This problem is non-trivial. In particular, it is challenging to derive the condition in terms of the communication channel quality and the LTI system parameters, to ensure a bounded long-term estimation MSE. We derive an elegant sufficient condition of the existence of a stationary and deterministic optimal policy that stabilizes the remote estimation system and minimizes the MSE. Also, we prove that the optimal policy has a switching structure, and accordingly derive a low-complexity suboptimal policy. Numerical results show that the proposed optimal policy significantly improves the performance of the remote estimation system compared to the conventional non-HARQ policy.