@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}
}

@inproceedings{정두식2026,
title = {하이브리드 빔포밍 시스템에서 PAPR 감소를 위한 예약 톤 설계 기법},
author = {정두식, 남현우, 김현우, 박현철},
year = {2026},
date = {2026-04-15},
booktitle = {KICS, 통신정보 합동학술대회, 여수, 대한민국, 2026},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}

@article{Kim2026,
title = {DeepMUTAT: Multiple Targets Tracking Algorithm using Multiple UAVs},
author = {S. Kim, S. Lim, J. Park, H. Park},
doi = {10.1109/TVT.2026.3669430},
issn = {1939-9359},
year = {2026},
date = {2026-03-03},
journal = {IEEE Transactions on Vehicular Technology},
abstract = {Unmanned aerial vehicle (UAV)-assisted networks are a promising technology in future wireless communication networks. Numerous applications related to UAV-assisted networks include mobile users (or targets) such as people, selfdriving vehicles, and robots. The location information of mobile devices is an essential element for realizing these applications. Furthermore, for providing services to mobile devices that move over time, real-time multi-target tracking is necessary. To ensure fair service provision to multiple users, unnecessary duplicated support or unsupported users should be avoided. For this reason, we consider multiple-target assignment constraints to fairly provide services. These motivations lead to the design of multiple UAV target assignment and tracking (MUTAT). In this paper, we propose a joint multi-UAV target assignment and tracking scheme to minimize target tracking errors while ensuring multiple target assignment constraints. Our proposed approach, named deep reinforcement learning-based multi-UAV target assignment and tracking (DeepMUTAT), consists of two distinct stages. In the first stage, to reduce computational complexity and ensure multiple target assignment constraints, we adopt a deep reinforcement learning (DRL)-based multi-target assignment for efficient multitarget tracking. In the second stage, to minimize tracking errors without requiring knowledge of the dynamics of targets and to avoid collisions with surrounding obstacles, we propose a DRL-based multi-target tracking approach. Based on extensive simulations, we demonstrate the performance of the proposed scheme compared to baseline schemes in terms of tracking error and tracking success probability. The proposed scheme achieved similar performance to the baseline scheme but had lower computational complexity.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}

@article{Hwang2026,
title = {Intelligent Read Framework With Meta-Learning for NAND Flash Memory Under Process Variation},
author = {M. Hwang, I. Choi, J. Jee, H. Park},
doi = {10.1109/ACCESS.2026.3664803},
issn = {2169-3536},
year = {2026},
date = {2026-02-16},
journal = {IEEE Access},
volume = {14},
pages = {26363 - 26380},
abstract = {The storage density of NAND flash memory has significantly increased due to multi-leveling and downscaling technologies. As a side effect of enhanced storage capacity, flash memory becomes vulnerable to circuit-level noise. To enhance data reliability, NAND flash memory employs error correction codes such as low-density parity-check (LDPC) code with soft-decision (SD) decoding based on log-likelihood ratio (LLR) information. Conventional SD read uses pre-defined uniform quantization, which is not optimized for varying threshold voltage distributions caused by physical noise, degradation, and process variation (PV). We propose an intelligent read framework employing a non-uniform quantizer that adjusts read reference voltages (RRVs) and LLR values according to the memory channel state. The optimization of RRVs and LLRs requires information on threshold voltage distributions. To accurately estimate the threshold voltage distribution from limited channel information, we propose a machine learning-based estimation model. Moreover, we incorporate model-agnostic meta-learning, enabling precise estimation of threshold voltage distribution across different flash chips addressing PV. Numerical results demonstrate significant improvements in read performance, validating the effectiveness of the proposed framework.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}