@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}
}

@inproceedings{이승훈2025,
title = {사용자 전력 상한 대비 오프로딩 지연시간 및 드롭아웃 성능 비교},
author = {이승훈, 박현철},
issn = {2383-8302},
year = {2025},
date = {2025-11-19},
booktitle = {KICS, 추계종합학술발표회, 경주, 대한민국, 2025},
journal = {KICS, 추계종합학술발표회, 경주, 2025},
pages = {3-4},
abstract = {본 논문은 셀프리(cell-free) 모바일 엣지 컴퓨팅(MEC) 환경에서 마감시간 제약 하의 작업 오프로딩, 연결, 빔포밍, 연산 자원할당을 통합적으로 최적화하는 프레임워크를 바탕으로, 사용자 전력 상한 변화가 드롭아웃 비율과 활성 사용자 평균 지연에 미치는 영향을 정량적으로 분석한다. 우리는 가중계수를 통해 드롭아웃 최소화와 지연 최소화 간의 상충관계를 동시에 조절하며, 업링크 무선·연산·오버헤드 요소를 동시에 반영한다. 시뮬레이션 결과, 사용자 전력이 15 dBm 으로 낮아지는 저전력 구간에서도 제안 기법은 두 기준 기법 대비 더 낮은 평균 지연과 드롭아웃 비율을 달성함을 확인하였다.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@article{A.Ahmadian2025,
title = {On the optimal duplexing strategy for wireless-powered communication networks},
author = {A.Ahmadian, H.Park},
doi = {https://doi.org/10.1016/j.phycom.2025.102729},
issn = {1874-4907},
year = {2025},
date = {2025-07-22},
journal = {Physical Communication},
volume = {72},
abstract = {Due to its simplicity and lack of channel state information (CSI) feedback requirements, time division duplexing (TDD) has been the preferred duplexing method in wireless-powered communication networks (WPCNs), while the advantages of frequency-division duplexing (FDD) has remained largely unexplored. Yet, the decision between TDD and FDD goes beyond CSI considerations, as it depends on various system parameters and operational trade-offs not previously considered. In FDD, the transmitter remains active throughout the entire frame duration, enabling more effective utilization of the maximum instantaneous transmit power of the hybrid access point (HAP). In contrast, TDD exploits the full bandwidth (BW), thereby making better use of the feasible maximum power spectral density (PSD). Finally, while the constraint of maximum time-averaged transmit power in FDD closely resembles the effect of the maximum instantaneous transmit power constraint, they have different effects on the operation of the TDD-WPCN.
To analyze these effects, we thoroughly investigate both TDD-WPCN and FDD-WPCN and characterize their respective operating regions. Our extensive theoretical and simulation results reveal that selecting between the two schemes involves a complex, multidimensional trade-off, warranting careful consideration in system design. We demonstrate that, under certain assumptions, the throughput of an FDD-WPCN can be substantially greater than that of the same WPCN operating in TDD mode. Furthermore we prove that, under certain conditions, a single-node FDD-WPCN can achieve a two-fold increase in throughput compared to a single-node TDD-WPCN.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}