The internet of vehicles (IoV) is rapidly evolving into an artificial intelligence (AI)-driven, multi-agent collaborative ecosystem, yet traditional communication paradigms struggle to address the challenges posed by dynamic network topologies, stringent resource constraints, and heterogeneous service demands. To bridg...
Qiong Wu, Wen-Jun Zhang, Ping-Yi Fan et al.· IEEE Communications Surveys...· 5 citations
This paper investigates how to reduce the amount of transmitted data during interactions with MLLMs while preserving their multimodal understanding performance, and proposes a token communication framework tailored to MLLMs.
Jing-Kai Ying, Zhi-Jin Qin, Yuan Shen et al.· 0 citations
This paper analytically characterize the progressive token-reconstruction error and derive a diffusion-step activation threshold that predicts when SpaceDiffusion is expected to outperform conventional decode-and-forward (DF) relaying, and develops an energy-aware early-exit policy to efficiently deploy SpaceDiffusion...
Jian-Hao Huang, Zhan-Wei Wang, K. Letaief et al.· 0 citations
A 3D Gaussian splatting for environment-aware beamforming (GSBF) pipeline based on multi-modal data, which characterizes the environment through a persistent 3D Gaussian representation, which consistently outperforms baselines such as exhaustive beam alignment (EBA) with lower latency.
Yi-Jie Bian, Wei Guo, Zi-Xin Wang et al.· 1 citation
Federated Learning (FL) has emerged as a transformative distributed learning paradigm, offering a privacy-preserving solution for collaborative model training. However, its conventional cloud-centric architecture suffers from a significant communication bottleneck casused by frequent model transmissions and heterogeneo...
Yuchen Shi, Qi-Jun Hou, Ping-Yi Fan et al.· IEEE Transactions on Mobile...· 0 citations
A hybrid-precision task-oriented communication framework for edge inference to holistically balance communication, on-device computation, and utility is proposed and confirmed that this design achieves an optimal trade-off among communication efficiency, on-device computational cost, and inference accuracy.
Songjie Xie, Wei Guo, Sheng-Hui Song et al.· arXiv.org· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
