6 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…
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…
FedFeat+: A Robust Federated Learning Framework Through Federated Aggregation and Differentially Private Feature-Based Classifier Retraining
Mrityunjoy Gain, Kitae Kim, Avi Deb Raha +4
In this paper, we propose the FedFeat+ framework, which distinctively separates feature extraction from classification. We develop a two-tiered model training process: following lo…
Security Risks in Vision-Based Beam Prediction: From Spatial Proxy Attacks to Feature Refinement
Avi Deb Raha, Kitae Kim, Mrityunjoy Gain +4
The rapid evolution towards the sixth-generation (6G) networks demands advanced beamforming techniques to address challenges in dynamic, high-mobility scenarios, such as vehicular…
Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks
Yu Qiao, Apurba Adhikary, Kitae Kim +3
Federated learning (FL) is a distributed training technology that enhances data privacy in mobile edge networks by allowing data owners to collaborate without transmitting raw data…
Cyber Attacks Prevention Towards Prosumer-based EV Charging Stations: An Edge-assisted Federated Prototype Knowledge Distillation Approach
Luyao Zou, Quang Hieu Vo, Kitae Kim +4
In this paper, cyber-attack prevention for the prosumer-based electric vehicle (EV) charging stations (EVCSs) is investigated, which covers two aspects: 1) cyber-attack detection o…