Multi-Sensor Scheduling for Remote State Estimation over Wireless MIMO Fading Channels with Semantic Over-the-Air Aggregation

TL;DR

This paper introduces a semantic over-the-air aggregation approach for multi-sensor remote state estimation over wireless MIMO channels, optimizing scheduling for power efficiency and estimation accuracy.

Contribution

It proposes a novel semantic scheduling policy based on dynamic programming and PSD cone decomposition, improving power efficiency and estimation performance.

Findings

Outperforms existing methods in estimation accuracy

Enhances power efficiency in sensor scheduling

Provides a practical low-complexity estimation algorithm

Abstract

In this work, we study multi-sensor scheduling for remote state estimation over wireless multiple-input multiple-output (MIMO) fading channels using a novel semantic over-the-air (SemOTA) aggregation approach. We first revisit Kalman filtering with conventional over-the-air (OTA) aggregation and highlight its transmit power limitations. To balance power efficiency and estimation performance, we formulate the scheduling task as a finite-horizon dynamic programming (DP) problem. By analyzing the structure of the optimal Q-function, we show that the resulting scheduling policy exhibits a semantic structure that adapts online to the estimation error covariance and channel variations. To obtain a practical solution, we derive a tractable upper bound on the Q-function via a positive semidefinite (PSD) cone decomposition, which enables an efficient approximate scheduling policy and a low-complexity remote estimation algorithm. Numerical results confirm that the proposed scheme outperforms existing methods in both estimation accuracy and power efficiency.