6 papers · 1 filter
AI/ML for mobile networks: Current status in Rel. 19 and challenges ahead
Yuan Gao, Xinyi Wu, Jun Jiang +6
The transformative power of artificial intelligence (AI) and machine learning (ML) is recognized as a key enabler for sixth generation (6G) mobile networks by both academia and ind…
CSI-MAE: A Masked Autoencoder-based Channel Foundation Model
Jun Jiang, Xiaolong Ruan, Shugong Xu
Self-Supervised Learning (SSL) has emerged as a key technique in machine learning, tackling challenges such as limited labeled data, high annotation costs, and variable wireless ch…
Enhanced Fingerprint-based Positioning With Practical Imperfections: Deep learning-based approaches
Shugong Xu, Jun Jiang, Wenjun Yu +7
High-precision positioning is vital for cellular networks to support innovative applications such as extended reality, unmanned aerial vehicles (UAVs), and industrial Internet of T…
C2S-AE: CSI to Sensing enabled by an Auto-Encoder-based Framework
Jun Jiang, Shugong Xu, Wenjun Yu +1
Next-generation mobile networks are set to utilize integrated sensing and communication (ISAC) as a critical technology, providing significant support for sectors like the industri…
A MIMO Wireless Channel Foundation Model via CIR-CSI Consistency
Jun Jiang, Wenjun Yu, Yunfan Li +2
In the field of artificial intelligence, self-supervised learning has demonstrated superior generalization capabilities by leveraging large-scale unlabeled datasets for pretraining…
MTCA: Multi-Task Channel Analysis for Wireless Communication
Jun Jiang, Wenjun Yu, Yuan Gao +1
In modern wireless communication systems, the effective processing of Channel State Information (CSI) is crucial for enhancing communication quality and reliability. However, curre…