CSI-Free Low-Complexity Remote State Estimation over Wireless MIMO Fading Channels using Semantic Analog Aggregation

TL;DR

This paper proposes a low-complexity, CSI-free remote state estimation method over wireless MIMO channels using semantic analog aggregation and constant-gain filtering, achieving stable and efficient estimation without prior CSI knowledge.

Contribution

It introduces a novel semantic aggregation technique and a constant-gain filtering algorithm optimized via CSSCA, eliminating the need for channel state information in MIMO systems.

Findings

Significant performance improvements over existing methods.

Stable estimation guaranteed under derived conditions.

Reduced computational complexity with constant-gain filtering.

Abstract

In this work, we investigate low-complexity remote system state estimation over wireless multiple-input-multiple-output (MIMO) channels without requiring prior knowledge of channel state information (CSI). We start by reviewing the conventional Kalman filtering-based state estimation algorithm, which typically relies on perfect CSI and incurs considerable computational complexity. To overcome the need for CSI, we introduce a novel semantic aggregation method, in which sensors transmit semantic measurement discrepancies to the remote state estimator through analog aggregation. To further reduce computational complexity, we introduce a constant-gain-based filtering algorithm that can be optimized offline using the constrained stochastic successive convex approximation (CSSCA) method. We derive a closed-form sufficient condition for the estimation stability of our proposed scheme via Lyapunov drift analysis. Numerical results showcase significant performance gains using the proposed scheme compared to several widely used methods.