11 citations · 12 across the 7 of their papers we have counts for
8 papers
Vision and Causal Learning Based Channel Estimation for THz Communications
Kitae Kim, Yan Kyaw Tun, Md. Shirajum Munir +3
The use of terahertz (THz) communications with massive multiple input multiple output (MIMO) systems in 6G can potentially provide high data rates and low latency communications. H…
Cross-Domain Federated Semantic Communication with Global Representation Alignment and Domain-Aware Aggregation
Loc X. Nguyen, Ji Su Yoon, Huy Q. Le +7
Semantic communication can significantly improve bandwidth utilization in wireless systems by exploiting the meaning behind raw data. However, the advancements achieved through sem…
DD-JSCC: Dynamic Deep Joint Source-Channel Coding for Semantic Communications
Avi Deb Raha, Apurba Adhikary, Mrityunjoy Gain +3
Deep Joint Source-Channel Coding (Deep-JSCC) has emerged as a promising semantic communication approach for wireless image transmission by jointly optimizing source and channel cod…
Resource-Efficient Beam Prediction in mmWave Communications with Multimodal Realistic Simulation Framework
Yu Min Park, Yan Kyaw Tun, Eui-Nam Huh +2
Beamforming is a key technology in millimeter-wave (mmWave) communications that improves signal transmission by optimizing directionality and intensity. However, conventional chann…
A Deep Incremental Framework for Multi-Service Multi-Modal Devices in NextG AI-RAN Systems
Mrityunjoy Gain, Kitae Kim, Avi Deb Raha +4
In this paper, we propose a deep incremental framework for efficient RAN management, introducing the Multi-Service-Modal UE (MSMU) system, which enables a single UE to handle eMBB…
Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning
Sheikh Salman Hassan, Yu Min Park, Yan Kyaw Tun +3
In this paper, a novel generative adversarial imitation learning (GAIL)-powered policy learning approach is proposed for optimizing beamforming, spectrum allocation, and remote use…
Design Optimization of NOMA Aided Multi-STAR-RIS for Indoor Environments: A Convex Approximation Imitated Reinforcement Learning Approach
Yu Min Park, Sheikh Salman Hassan, Yan Kyaw Tun +3
Non-orthogonal multiple access (NOMA) enables multiple users to share the same frequency band, and simultaneously transmitting and reflecting reconfigurable intelligent surface (ST…
SpaFL: Communication-Efficient Federated Learning with Sparse Models and Low computational Overhead
Minsu Kim, Walid Saad, Merouane Debbah +1
The large communication and computation overhead of federated learning (FL) is one of the main challenges facing its practical deployment over resource-constrained clients and syst…