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

Minjie Tang, Photios A. Stavrou, Marios Kountouris

TL;DR

This work tackles remote state estimation over wireless MIMO fading channels without channel state information, addressing both computational complexity and CSI overhead. It introduces semantic analog aggregation, where sensors transmit innovations rather than raw measurements, enabling a constant-gain remote estimator optimized offline via CSSCA. Stability is established through Lyapunov drift analysis, yielding a tractable condition on the gain that ensures bounded estimation error. Numerical results show significant improvements in NMSE, power efficiency, and CPU time over CSI-based Kalman filtering and analog aggregation baselines, highlighting the practical impact for scalable, low-latency remote estimation in wireless networks.

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.

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

TL;DR

This work tackles remote state estimation over wireless MIMO fading channels without channel state information, addressing both computational complexity and CSI overhead. It introduces semantic analog aggregation, where sensors transmit innovations rather than raw measurements, enabling a constant-gain remote estimator optimized offline via CSSCA. Stability is established through Lyapunov drift analysis, yielding a tractable condition on the gain that ensures bounded estimation error. Numerical results show significant improvements in NMSE, power efficiency, and CPU time over CSI-based Kalman filtering and analog aggregation baselines, highlighting the practical impact for scalable, low-latency remote estimation in wireless networks.

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.
Paper Structure (18 sections, 29 equations, 4 figures, 2 algorithms)

This paper contains 18 sections, 29 equations, 4 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Dynamic Plant Model
  4. Wireless Sensor Model
  5. Semantic Signal Extraction and Wireless Communication Channel Model
  6. State Estimation at the Remote State Estimator
  7. CSI-Free Low-Complexity Remote State Estimation via Semantic Analog Aggregation
  8. Generation of Semantic Information at Sensors
  9. CSI-Free Low-Complexity State Estimation Algorithm at the Remote State Estimator
  10. Estimation Stability Analysis
  11. Optimization of Filtering Gain
  12. Numerical Results
  13. NMSE versus the Number of Sensors
  14. Total Transmission Power at Sensors versus Timeslot
  15. Total CPU Computational Time versus Plant Dimension
  16. ...and 3 more sections

Figures (4)

  • Figure 1: Typical architecture of a remote state estimation system over a wireless network.
  • Figure 2: Normalized state estimation MSE versus the number of sensors $M$.
  • Figure 3: Total transmission power at sensors versus timeslot. The number of sensors $M=6$.
  • Figure 4: Total CPU computational time versus plant dimension. $\mathbf{A}\in\mathbb{R}^{S\times S}$ and $\mathbf{C}_m\in\mathbb{R}^{N_t\times S}$ are generated with elements following a Gaussian distribution (zero mean, unit variance). The number of sensors $M=6$.

Theorems & Definitions (1)

  • Definition 1