7 papers
Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs
Xiaowen Cao, Zhonghao Lyu, Shicheng Chu +6
The increasing scale and computational demands of large artificial intelligence models (LAIMs) present significant challenges for efficient inference in resource-constrained distri…
BeamVLM for Low-altitude Economy: Generative Beam Prediction via Vision-language Models
Chenran Kou, Changsheng You, Mingjiang Wu +3
For low-altitude economy (LAE), fast and accurate beam prediction between high-mobility unmanned aerial vehicles (UAVs) and ground base stations is of paramount importance, which e…
EMS-FL: Federated Tuning of Mixture-of-Experts in Satellite-Terrestrial Networks via Expert-Driven Model Splitting
Angzi Xu, Zezhong Zhang, Zhi Liu +1
The rapid advancement of large AI models imposes stringent demands on data volume and computational resources. Federated learning, though designed to exploit distributed data and c…
RadioGen3D: 3D Radio Map Generation via Adversarial Learning on Large-Scale Synthetic Data
Junshen Chen, Angzi Xu, Zezhong Zhang +3
Radio maps are essential for efficient radio resource management in future 6G and low-altitude networks. While deep learning (DL) techniques have emerged as an efficient alternativ…
RadioDiff-3D: A 3D3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communication
Xiucheng Wang, Qiming Zhang, Nan Cheng +5
Radio maps (RMs) serve as a critical foundation for enabling environment-aware wireless communication, as they provide the spatial distribution of wireless channel characteristics.…
Fast and Accurate Cooperative Radio Map Estimation Enabled by GAN
Zezhong Zhang, Guangxu Zhu, Junting Chen +1
In the 6G era, real-time radio resource monitoring and management are urged to support diverse wireless-empowered applications. This calls for fast and accurate estimation on the d…