30 citations · 33 across the 19 of their papers we have counts for
19 papers
Rethinking the Foundations of Two-Sided AI Models for 6G
Yongjeong Oh, Zihan Chen, Timothy J. O'Shea +4
For next-generation air interfaces, two-sided artificial intelligence (AI) models have received growing attention, with AI models deployed at both the transmitter and receiver for…
Loss-Resilient Wireless Video Token Communication over Block Fading Channels
Bingyan Xie, Yongjeong Oh, Zihan Chen +3
Video token communication represents video content as discrete tokens that differ in their importance to reconstruction and exhibit temporal dependencies. When these tokens are pac…
Geometric Cross-Modal Token Selection for Latency-Constrained Multimodal Token Communication
Joohyuk Park, Junyong Shin, Yongjeong Oh +2
This paper proposes a geometry-based joint cross-modal token selection framework for latency-constrained multimodal token communications. To capture cross-modal token dependencies,…
Context-Aware Wireless Token Communication via Joint Token Masking and Detection
Junyong Shin, Joohyuk Park, Yongjeong Oh +3
The increasing use of token-based representations in language-driven applications has motivated wireless token communication, where tokens are treated as fundamental units for tran…
Semantic Feature Channel Optimization: A Unified Framework for Analog and Digital Semantic Communications
Yongjeong Oh, Jihong Park, Jinho Choi +1
Semantic communication (SC) aims to improve communication efficiency by transmitting only task-relevant information, termed semantic features (SFs). Most existing SC frameworks ach…
Rate-Adaptive Semantic Communication via Multi-Stage Vector Quantization
Jinsung Park, Junyong Shin, Yongjeong Oh +2
This paper proposes a novel framework for rate-adaptive semantic communication based on multi-stage vector quantization (VQ), termed \textit{MSVQ-SC}. Unlike conventional single-st…
Importance-Aware Semantic Communication in MIMO-OFDM Systems Using Vision Transformer
Joohyuk Park, Yongjeong Oh, Jihun Park +1
This paper presents a novel importance-aware quantization, subcarrier mapping, and power allocation (IA-QSMPA) framework for semantic communication in multiple-input multiple-outpu…
Unequal Error Protection for Digital Semantic Communication with Channel Coding
Seonjung Kim, Yongjeong Oh, Yongjune Kim +2
This paper investigates unequal error protection (UEP) in digital semantic communication, where semantically important bits require substantially higher reliability than less criti…
ESC-MVQ: End-to-End Semantic Communication With Multi-Codebook Vector Quantization
Junyong Shin, Yongjeong Oh, Jinsung Park +2
This paper proposes a novel end-to-end digital semantic communication framework based on multi-codebook vector quantization (VQ), referred to as ESC-MVQ. Unlike prior approaches th…
Blind Training for Channel-Adaptive Digital Semantic Communications
Yongjeong Oh, Joohyuk Park, Jinho Choi +2
Semantic encoders and decoders for digital semantic communication (SC) often struggle to adapt to variations in unpredictable channel environments and diverse system designs. To ad…
Vision Transformer-based Semantic Communications With Importance-Aware Quantization
Joohyuk Park, Yongjeong Oh, Yongjune Kim +1
Semantic communications provide significant performance gains over traditional communications by transmitting task-relevant semantic features through wireless channels. However, mo…
Deep Learning-Assisted Parallel Interference Cancellation for Grant-Free NOMA in Machine-Type Communication
Yongjeong Oh, Jaehong Jo, Byonghyo Shim +1
In this paper, we present a novel approach for joint activity detection (AD), channel estimation (CE), and data detection (DD) in uplink grant-free non-orthogonal multiple access (…
Prior-Aware Robust Beam Alignment for Low-SNR Millimeter-Wave Communications
Jihun Park, Yongjeong Oh, Jaewon Yun +2
This paper presents a robust beam alignment technique for millimeter-wave communications in low signal-to-noise ratio (SNR) environments. The core strategy of our technique is to r…
Joint Source-Channel Coding for Channel-Adaptive Digital Semantic Communications
Joohyuk Park, Yongjeong Oh, Seonjung Kim +1
In this paper, we propose a novel joint source-channel coding (JSCC) approach for channel-adaptive digital semantic communications. In semantic communication systems with digital m…
SplitMAC: Wireless Split Learning over Multiple Access Channels
Seonjung Kim, Yongjeong Oh, Yo-Seb Jeon
This paper presents a novel split learning (SL) framework, referred to as SplitMAC, which reduces the latency of SL by leveraging simultaneous uplink transmission over multiple acc…
Communication-Efficient Federated Learning over Capacity-Limited Wireless Networks
Jaewon Yun, Yongjeong Oh, Yo-Seb Jeon +1
In this paper, a communication-efficient federated learning (FL) framework is proposed for improving the convergence rate of FL under a limited uplink capacity. The central idea of…
Communication-Efficient Split Learning via Adaptive Feature-Wise Compression
Yongjeong Oh, Jaeho Lee, Christopher G. Brinton +1
This paper proposes a novel communication-efficient split learning (SL) framework, named SplitFC, which reduces the communication overhead required for transmitting intermediate fe…
FedVQCS: Federated Learning via Vector Quantized Compressed Sensing
Yongjeong Oh, Yo-Seb Jeon, Mingzhe Chen +1
In this paper, a new communication-efficient federated learning (FL) framework is proposed, inspired by vector quantized compressed sensing. The basic strategy of the proposed fram…
Communication-Efficient Federated Learning via Quantized Compressed Sensing
Yongjeong Oh, Namyoon Lee, Yo-Seb Jeon +1
In this paper, we present a communication-efficient federated learning framework inspired by quantized compressed sensing. The presented framework consists of gradient compression…