4 citations · 7 across the 5 of their papers we have counts for
9 papers
MetaSSD: Meta-Learned Self-Supervised Detection
Moon Jeong Park, Jungseul Ok, Yo-Seb Jeon +1
Deep learning-based symbol detector gains increasing attention due to the simple algorithm design than the traditional model-based algorithms such as Viterbi and BCJR. The supervis…
Semi-Data-Aided Channel Estimation for MIMO Systems via Reinforcement Learning
Tae-Kyoung Kim, Yo-Seb Jeon, Jun Li +2
Data-aided channel estimation is a promising solution to improve channel estimation accuracy by exploiting data symbols as pilot signals for updating an initial channel estimate. I…
Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization
Kang Wei, Jun Li, Ming Ding +3
Federated learning (FL), as a type of distributed machine learning frameworks, is vulnerable to external attacks on FL models during parameters transmissions. An attacker in FL may…
Data-Aided Channel Estimator for MIMO Systems via Reinforcement Learning
Yo-Seb Jeon, Jun Li, Nima Tavangaran +1
This paper presents a data-aided channel estimator that reduces the channel estimation error of the conventional linear minimum-mean-squared-error (LMMSE) method for multiple-input…
A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems
Yo-Seb Jeon, Mohammad Mohammadi Amiri, Jun Li +1
Federated learning is a privacy-preserving approach to train a global model at a central server by collaborating with wireless devices, each with its own local training data set. I…
Robust Data Detection for MIMO Systems with One-Bit ADCs: A Reinforcement Learning Approach
Yo-Seb Jeon, Namyoon Lee, H. Vincent Poor
The use of one-bit analog-to-digital converters (ADCs) at a receiver is a power-efficient solution for future wireless systems operating with a large signal bandwidth and/or a mass…