@article{Son2025,
title = {A Novel Iterative Hybrid Beamforming Design With Per-RF Chain Power Constraints in Massive MIMO OFDM Systems},
author = {H. Son, G. Kwon, J. Park, H. Nam, and H. Park},
doi = {10.1109/TVT.2025.3574475},
year = {2025},
date = {2025-06-12},
journal = {IEEE Transactions on Vehicular Technology},
volume = {Early Access},
abstract = {One of representative strategies for hybrid analog-and-digital beamforming (HBF) in massive multiple-input multiple-output (MIMO) systems is the two-stage design, where radio frequency (RF) and baseband (BB) beamformers are optimized sequentially. However, the two-stage approach overlooks the impact of BB beamforming (BF) on RF BF design, resulting in loss of optimality of RF BF. To address this, we propose an iterative two-stage HBF design that considers the influence of BB BF on RF BF design. The BB BF is designed per subcarrier for orthogonal frequency division multiplexing (OFDM) systems and optimized under per-RF chain power constraints. Simulation results verify superiority of the proposed HBF design.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@inproceedings{Lee2025,
title = {Semantic Packet Aggregation and Repeated Transmission for Text-to-Image Generation},
author = {S. Lee, J. Park, J. Choi, H. Park},
year = {2025},
date = {2025-06-08},
organization = {IEEE International Conference on Communications (ICC) 2025, Montreal, Canada},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}

@inproceedings{Kim2025,
title = {Robust ISAC Beamformer Utilizing Angular Distributions of Target and Clutters},
author = {H. Kim, J. Jee, H. Park},
year = {2025},
date = {2025-06-08},
organization = {IEEE International Conference on Communications (ICC) 2025 Workshops, Montreal, Canada},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}

@article{Lee2025b,
title = {Distributed Multi-Agent Reinforcement Learning for Scalable Cell-Free MIMO Networks},
author = {S. Lee, G. Kwon, J. Park, H. Park},
doi = {10.1109/TWC.2025.3571465},
issn = {1558-2248},
year = {2025},
date = {2025-05-30},
journal = {IEEE Transactions on Wireless Communications},
volume = {Early Access},
abstract = {Cell-free multiple-input-multiple-output (MIMO) is poised to enable scalable next-generation cellular networks. To this end, it is crucial to optimize the cell-free MIMO link configuration, including user associations, data stream allocation, and beamforming (BF). However, the scalability of link configuration optimization is significantly challenged as signaling and computational costs increase with the number of base stations (BSs) and user equipments (UEs). To address this scalability issue, this paper proposes a distributed multi-agent deep reinforcement learning (MADRL)-based cell-free MIMO link configuration framework that leverages interference approximation to minimize signaling overhead required for channel state information (CSI) exchange. Our proposed framework reduces the solution search space suitable for distributed MADRL, by decomposing the original sum rate maximization problem into BS-specific tasks. Simulation results show that our proposed method achieves scalability, as the sum rate increases with the number of BSs and UEs.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}