Zobrazeno 1 - 10
of 287
pro vyhledávání: '"Kang, Joonhyuk"'
Given sufficient data from multiple edge devices, federated learning (FL) enables training a shared model without transmitting private data to a central server. However, FL is generally vulnerable to Byzantine attacks from compromised edge devices, w
Externí odkaz:
http://arxiv.org/abs/2411.10212
Federated learning enables edge devices to collaboratively train a global model while maintaining data privacy by keeping data localized. However, the Non-IID nature of data distribution across devices often hinders model convergence and reduces perf
Externí odkaz:
http://arxiv.org/abs/2410.23824
Integrating hyperscale AI into national defense modeling and simulation (M&S) is crucial for enhancing strategic and operational capabilities. We explore how hyperscale AI can revolutionize defense M\&S by providing unprecedented accuracy, speed, and
Externí odkaz:
http://arxiv.org/abs/2410.00367
Enhancing Battlefield Awareness: An Aerial RIS-assisted ISAC System with Deep Reinforcement Learning
This paper considers a joint communication and sensing technique for enhancing situational awareness in practical battlefield scenarios. In particular, we propose an aerial reconfigurable intelligent surface (ARIS)-assisted integrated sensing and com
Externí odkaz:
http://arxiv.org/abs/2405.20168
Detecting occupied subbands is a key task for wireless applications such as unlicensed spectrum access. Recently, detection methods were proposed that extract per-subband features from sub-Nyquist baseband samples and then apply thresholding mechanis
Externí odkaz:
http://arxiv.org/abs/2405.17071
Autor:
Seo, Jiwan, Kang, Joonhyuk
Vector Quantized Variational AutoEncoder (VQ-VAE) is an established technique in machine learning for learning discrete representations across various modalities. However, its scalability and applicability are limited by the need to retrain the model
Externí odkaz:
http://arxiv.org/abs/2405.14222
Autor:
Jung, Sooyeob, Jeong, Seongah, Kang, Jinkyu, Im, Gyeongrae, Lee, Sangjae, Oh, Mi-Kyung, Ryu, Joon Gyu, Kang, Joonhyuk
This paper proposes a long range-frequency hopping spread spectrum (LR-FHSS) transceiver design for the Direct-to-Satellite Internet of Things (DtS-IoT) communication system. The DtS-IoT system has recently attracted attention as a promising nonterre
Externí odkaz:
http://arxiv.org/abs/2403.14154
In this correspondence, we propose hierarchical high-altitude platform (HAP)-low-altitude platform (LAP) networks with the aim of maximizing the sum-rate of ground user equipments (UEs). The multiple aerial radio units (RUs) mounted on HAPs and LAPs
Externí odkaz:
http://arxiv.org/abs/2312.04081
Foundation models (FMs) have demonstrated remarkable performance in machine learning but demand extensive training data and computational resources. Federated learning (FL) addresses the challenges posed by FMs, especially related to data privacy and
Externí odkaz:
http://arxiv.org/abs/2310.14579
Accurate uncertainty quantification in graph neural networks (GNNs) is essential, especially in high-stakes domains where GNNs are frequently employed. Conformal prediction (CP) offers a promising framework for quantifying uncertainty by providing $\
Externí odkaz:
http://arxiv.org/abs/2310.11479