3 papers
eess.SP2026
Attention-Aided MMSE with Ridge Denoising: How to Train under Noisy Channel Samples
TaeJun Ha, Hyeji Kim, Jeonghun Park
Deep neural channel estimators are typically trained with clean channel state information (CSI), which is unavailable in practical orthogonal frequency-division multiplexing (OFDM)…
eess.SP2025
Energy-Efficient State Estimation with 1-Bit Sensing: A Bussgang-Kalman Framework for Internet of Things
Chaehyun Jung, TaeJun Ha, Hyeonuk Kim +1
Accurate state estimation from heavily quantized measurements is a key challenge in resource-constrained Internet of Things (IoT) sensing and tracking, where battery-powered device…
eess.SP2025
Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference
TaeJun Ha, Chaehyun Jung, Hyeonuk Kim +2
In orthogonal frequency division multiplexing (OFDM), accurate channel estimation is crucial. Classical signal processing-based approaches, such as linear minimum mean-squared erro…