Agent-driven Generative Semantic Communication with Cross-Modality and Prediction

Wanting Yang, Zehui Xiong*, Yanli Yuan*, Wenchao Jiang, Tony Q.S. Quek, Merouane Debbah

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

摘要

In the era of 6G, with compelling visions of intelligent transportation systems and digital twins, remote surveillance is poised to become a ubiquitous practice. Substantial data volume and frequent updates present challenges in wireless networks. To address these challenges, we propose a novel agent-driven generative semantic communication (A-GSC) framework based on reinforcement learning. In contrast to the existing research on semantic communication (SemCom), which mainly focuses on either semantic extraction or semantic sampling, we seamlessly integrate both by jointly considering the intrinsic attributes of source information and the contextual information regarding the task. Notably, the introduction of generative artificial intelligence (GAI) enables the independent design of semantic encoders and decoders. In this work, we develop an agent-assisted semantic encoder with cross-modality capability, which can track the semantic changes, channel condition, to perform adaptive semantic extraction and sampling. Accordingly, we design a semantic decoder with both predictive and generative capabilities, consisting of two tailored modules. Moreover, the effectiveness of the designed models has been verified using the UA-DETRAC dataset, demonstrating the performance gains of the overall A-GSC framework in both energy saving and reconstruction accuracy.

源语言英语
期刊IEEE Transactions on Wireless Communications
DOI
出版状态已接受/待刊 - 2024

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引用此

Yang, W., Xiong, Z., Yuan, Y., Jiang, W., Quek, T. Q. S., & Debbah, M. (已接受/印刷中). Agent-driven Generative Semantic Communication with Cross-Modality and Prediction. IEEE Transactions on Wireless Communications. https://doi.org/10.1109/TWC.2024.3519325