9 papers
JEPA for AI-Native 6G: Predictive Representations and Open Challenges
Sheikh Salman Hassan, Irshad A. Meer, Almoatssimbillah Saifaldawla +9
Sixth-generation (6G) networks are moving toward AI-native operation, where learning modules are embedded across the radio access network (RAN), edge, and core. This transition req…
Empowering Embodied AI in 6G Networks: Architecture, Enablers, and Open Challenges
Junaid Sajid, Sheikh Salman Hassan, Wenshuai Liu +7
Embodied artificial intelligence (AI) is emerging as a key driver of the sixth-generation (6G) wireless networks by enabling agents that continuously perceive, communicate, and act…
Geometric Knowledge-Assisted Federated Dual Knowledge Distillation Approach Towards Remote Sensing Satellite Imagery
Luyao Zou, Fei Pan, Jueying Li +4
Federated learning (FL) has recently become a promising solution for analyzing remote sensing satellite imagery (RSSI). However, the large scale and inherent data heterogeneity of…
A Contrastive Variational AutoEncoder for NSCLC Survival Prediction with Missing Modalities
Michele Zanitti, Vanja Miskovic, Francesco Trovò +5
Predicting survival outcomes for non-small cell lung cancer (NSCLC) patients is challenging due to the different individual prognostic features. This task can benefit from the inte…
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…
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…