18 papers
Role-Adaptive Policy Optimization for Offline Reinforcement Learning
Seonvin Cho, Soohyun Choi, Songnam Hong
Policy regularization in offline reinforcement learning balances policy improvement against reliance on uncertain value estimates. This balance can differ between selecting actions…
Online Movable Antenna Repositioning Under Movement Delay and a Long-Term Energy Budget
Yunseob Tae, Jeongjae Lee, Songnam Hong
Movable antenna (MA) systems improve the channel by repositioning antennas, but each repositioning interrupts data transmission for a time set by the largest displacement and consu…
Is One Step Enough for Offline Policy Improvement?
Soohyun Choi, Seonvin Cho, Songnam Hong
Behavior regularization in offline reinforcement learning limits the exploitation of critic errors, but strong anchoring can also restrict policy improvement. We study how policy i…
PathBridger: Subgoal Bridges for Offline Goal-Conditioned Reinforcement Learning
Soohyun Choi, Seonvin Cho, Songnam Hong
Offline goal-conditioned reinforcement learning (GCRL) aims to learn policies for reaching diverse goals entirely from fixed trajectory data. Long-horizon offline GCRL remains chal…
Heterogeneous Multi-Agent Reinforcement Learning for Radio Resource Management under Coupled Finite-Horizon Constraints
Yeonseo Jeong, Wonhyeok Ko, Sungweon Hong +1
Maximizing throughput under proportional fairness in dense wireless networks requires jointly managing user association, scheduling, base station (BS) activation, and handover cont…
Scalable Rate-Splitting Precoding via Recurrent Structure-Preserving Graph Neural Networks
Wonseok Choi, Jeongjae Lee, Songnam Hong
Graph neural network (GNN)-based precoding has demonstrated strong potential for scalable multi-user beamforming in multi-user multiple-input single-output (MU-MISO) systems under…
Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries
Hyeong-Gun Joo, Songnam Hong, Dong-Joon Shin
On-device federated learning (FL) enables privacy-preserving and personalized model training on resource-constrained devices such as smartphones and IoT nodes. To reduce communicat…
CSIT-Free Beamforming for Multi-Group Multicast in Overloaded mmWave Systems
Wonseok Choi, Jeongjae Lee, Songnam Hong
We study downlink multi-group multicast (MGM) transmission in overloaded millimeter-wave (mmWave) systems, where the number of users exceeds the number of transmit antennas. We fir…
CSIT-Free Downlink Transmission for mmWave MU-MISO Systems in High-Mobility Scenario
Jeongjae Lee, Wonseok Choi, Songnam Hong
This paper investigates the downlink (DL) transmission in millimeter-wave (mmWave) multi-user multiple-input single-output (MU-MISO) systems especially focusing on a high speed mob…
Federated Reinforcement Learning in Heterogeneous Environments
Ukjo Hwang, Songnam Hong
We investigate a Federated Reinforcement Learning with Environment Heterogeneity (FRL-EH) framework, where local environments exhibit statistical heterogeneity. Within this framewo…
Moderate Actor-Critic Methods: Controlling Overestimation Bias via Expectile Loss
Ukjo Hwang, Songnam Hong
Overestimation is a fundamental characteristic of model-free reinforcement learning (MF-RL), arising from the principles of temporal difference learning and the approximation of th…
Blind Massive MIMO for Dense IoT Networks
Jeongjae Lee, Songnam Hong
In this paper, we investigate the downlink communication challenges in heavy-load Internet-of-Things (IoT) networks supported by frequency-division-duplexing (FDD) millimeter-wave…
Piecewise Beam Training and Channel Estimation for RIS-Aided Near-Field Communications
Jeongjae Lee, Songnam Hong
In this paper, we investigate the channel estimation challenge in reconfigurable intelligent surface (RIS)-aided near-field communication systems. Current channel estimation techni…
Near-Field LoS/NLoS Channel Estimation for RIS-Aided MU-MIMO Systems: Piece-Wise Low-Rank Approximation Approach
Jeongjae Lee, Songnam Hong
We study the channel estimation problem for a reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) multi-user multiple-input multiple-output (MU-MIMO) system.…
Asymptotically Near-Optimal Hybrid Beamforming for mmWave IRS-Aided MIMO Systems
Jeongjae Lee, Songnam Hong
Hybrid beamforming is an emerging technology for massive multiple-input multiple-output (MIMO) systems due to the advantages of lower complexity, cost, and power consumption. Recen…
Near-Field Channel Estimation for XL-RIS Assisted Multi-User XL-MIMO Systems: Hybrid Beamforming Architectures
Jeongjae Lee, Hyeongjin Chung, Yunseong Cho +2
Channel estimation is one of the key challenges for the deployment of extremely large-scale reconfigurable intelligent surface (XL-RIS) assisted multiple-input multiple-output (MIM…
On Practical Robust Reinforcement Learning: Practical Uncertainty Set and Double-Agent Algorithm
Ukjo Hwang, Songnam Hong
Robust reinforcement learning (RRL) aims at seeking a robust policy to optimize the worst case performance over an uncertainty set of Markov decision processes (MDPs). This set con…
Tighter Regret Analysis and Optimization of Online Federated Learning
Dohyeok Kwon, Jonghwan Park, Songnam Hong
In federated learning (FL), it is commonly assumed that all data are placed at clients in the beginning of machine learning (ML) optimization (i.e., offline learning). However, in…