To Retransmit or Not: Real-Time Remote Estimation in Wireless Networked Control

TL;DR

This paper develops a HARQ-based framework for real-time remote estimation in wireless control systems, balancing reliability and freshness of data to optimize estimation accuracy.

Contribution

It formulates a new decision policy optimization problem for retransmission strategies, proving the existence of an optimal policy and deriving a low-complexity suboptimal solution.

Findings

Optimal policy improves estimation accuracy over non-HARQ methods

Sufficient conditions for system stability and bounded MSE are provided

The optimal policy has a switching structure, enabling efficient implementation

Abstract

Real-time remote estimation is critical for mission-critical applications including industrial automation, smart grid, and the 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. When a failed transmission occurs, the sensor can either retransmit the previous old measurement such that the receiver can obtain a more reliable old measurement, or transmit a new but less reliable measurement. To design the optimal decision, we formulate a new problem to optimize the sensor's online decision policy, i.e., to retransmit or not, depending 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 not clear what the condition is in terms of the communication channel quality and the LTI system parameters, to ensure that the long-term estimation MSE can be bounded. We give a 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 derive a low-complexity suboptimal policy. Our numerical results show that the proposed optimal policy notably improves the performance of the remote estimation system compared to the conventional non-HARQ policy.