@inproceedings{이승훈2026,
title = {토큰 통신을 위한 선택적 대형언어모델 기반 텍스트 교정 방법},
author = {이승훈, 박지홍, 최진호, 박현철},
year = {2026},
date = {2026-06-17},
booktitle = {KICS, 하계종합학술발표회, 제주, 대한민국, 2026},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}

@inproceedings{M.Hwang2026,
title = {Exploiting Near-field Effect with Spatially Separated Sub-arrays for Upper Mid-band ISAC},
author = {M.Hwang, H.Park, N. Gonzalez-Preclic},
year = {2026},
date = {2026-05-24},
booktitle = {IEEE International Conference on Communications (ICC) 2026, Glascow, Scotland},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@article{Lee2026,
title = {Low-Complexity Semantic Packet Aggregation for Token Communication via Lookahead Search},
author = {S. Lee, J. Park, J.Choi, H. Park},
issn = {1558-0857},
year = {2026},
date = {2026-05-10},
journal = {IEEE Transactions on Communications},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}

@article{N.Hong2026,
title = {Deadline-Aware joint optimization of task offloading and resource allocation for cell-Free mobile edge computing networks},
author = {N.Hong, S.Lee, H.Park},
doi = {https://doi.org/10.1016/j.comnet.2026.112219},
issn = {1389-1286},
year = {2026},
date = {2026-05-01},
journal = {Computer Networks},
volume = {281},
number = {112219},
abstract = {The increasing demand for wireless applications with tight deadlines and intensive computational requirements has exposed the limitations of traditional mobile edge computing (MEC). In such a tight deadline task processing environment, it is inevitable that some tasks cannot be processed within the deadline and are dropped. To tackle this, we formulate an optimization problem that minimizes a weighted sum of the dropout ratio and average delay. To solve the problem, we present an integrated method that combines MEC with cell-free networks. In addition, we propose the Joint optimization of Association, Beamforming, Task Offloading, and Resource Allocation (JAB-TORA), which accounts for realistic wireless conditions. We formulate the problem as a Markov decision process and address it using a multi-agent deep reinforcement learning approach. Simulation results demonstrate the inversely proportional relationship between the dropout ratio and the average delay when adjusting the weights. These weights can be adjusted to meet the required dropout ratio or delay level. Furthermore, we demonstrate that the proposed method achieves a lower dropout ratio and delay than other offloading methods.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}