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...· 4 citations
With the broad success of the Transformer architecture, token is becoming a new basic information processing unit. This trend is especially evident in multimodal large language models (MLLMs), where both visual and textual information are represented and processed as tokens. With the rapid deployment of MLLMs, the effi...
Jing-Kai Ying, Zhi-Jin Qin, Yuan Shen et al.· 0 citations
Beamforming plays a key role in multiple-input-multiple-output (MIMO) communication systems. However, conventional beamforming design normally requires accurate instantaneous channel state information (CSI) and iterative optimization, which incur substantial pilot overhead and computational complexity. Recognizing that...
Yijie Bian, Wei Guo, Zixin Wang et al.· 0 citations
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
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