Deep Generative Channel Estimation for Ultra-wideband Multiple-input Multiple-output: Enabling Communication and Sensing in Industrial IoT

In complex indoor environments, ultra-wideband multiple-input multiple-output (UWB-MIMO) systems often suffer from limited channel estimation accuracy, which decreases communication capacity and sensing fidelity.To address this challenge and enhance joint channel estimation and capacity, a deep generative model based on a variational autoencoder was developed in this study.The model processes complex-valued channel matrices by separating real and imaginary components, and employs an encoder-decoder structure to learn the latent probabilistic distribution of multipath signatures.Simulation results showed that the variational autoencoder (VAE)-based model outperforms conventional least squares, minimum mean square error, and compressive sensing-based models across the entire signal-to-noise (SNR) ratio range.The model showed substantial gains in low-SNR regions, where sensing fidelity became vulnerable to noise, enhancing channel capacity to 10% at a typical SNR of 10 dB.By accurately recovering spatial structures while suppressing noise, the framework preserves critical multipath features essential for centimeter-level localization and precise ranging.The developed method utilizes UWB-MIMO to enable ultra-reliable communication and high-resolution sensing.These capabilities make VAE-based models applicable to Industrial IoT and smart manufacturing environments, where reliable connectivity and fine-grained environmental awareness are essential for asset tracking, worker safety monitoring, and material characterization.