Artificial intelligence (AI) and machine learning (ML)-based channel estimators silently degrade when propagation conditions drift from their training distributions. This letter proposes a model-agnostic cognitive digital twin (CDT) framework that combines a variational autoencoder (VAE) with latent activation monitoring to detect distribution drift and autonomously execute \textsc{continue}, \textsc{update}, or \textsc{retire} lifecycle actions without requiring ground-truth channel knowledge. The proposed framework is fully compatible with the AI-native lifecycle management envisioned in 3rd Generation Partnership Project (3GPP). Simulations over various channels demonstrate accurate drift detection and robust channel estimation, consistently outperforming conventional offline-trained deep learning estimators under moderate and severe channel drift.
Fast ray tracing (RT) has stimulated the Digital Twin (DT) as an emerging technology for environment-aware communications. Since wireless propagation is governed by the interaction between site geometry and electromagnetic (EM) properties of the environment, DT-based approaches c…
Digital twin channel (DTC) aims to establish a real-time digital counterpart of physical wireless channels for reproducing and predicting site-specific propagation characteristics. As a high-precision channel computation method for realistic propagation scenarios, ray tracing (RT…
This paper proposes a transformer-hypernetwork-controlled deep-unfolded phase-aware channel estimation refinement (THUNDER) for phase-drifting backscatter links. Residual carrier-phase drift across the pilot block renders the backscattered observation phase-nonstationary, and a c…
Data-aided channel estimation enhances spectral efficiency by reusing detected symbols as virtual pilots. In this process, selecting only reliable symbols is crucial to prevent misdetected symbols from corrupting the channel estimate. However, conventional methods rely exclusivel…
Microwave linear analog computers (MiLACs) have recently gained attention for future gigantic multiple-input multiple-output (MIMO) systems by enabling beamforming with greatly reduced hardware and computational cost. However, channel estimation for MiLAC-aided multiuser systems…
Accurate channel estimation in orthogonal frequency division multiplexing (OFDM) systems remains challenging when demodulation reference signal (DMRS) observations are sparse and noisy, and when DMRS configurations vary across deployment scenarios. This paper proposes DANCE (Diff…