ICC 2025, IEEE International Conference on Communications, 8-12 June 2025, Montreal, Canada
In this work, we investigate low-complexity remote system state estimation over wireless multiple-input-multipleoutput (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.
Type:
Conférence
City:
Montreal
Date:
2025-06-08
Department:
Systèmes de Communication
Eurecom Ref:
8039
Copyright:
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