智能体驱动的跨模态与预测生成式语义通信
Agent-driven Generative Semantic Communication with Cross-Modality and Prediction
AI总结：
本文提出基于强化学习的智能体驱动生成式语义通信框架A-GSC，通过跨模态语义编码器与预测生成解码器联合优化语义提取和采样，在UA-DETRAC数据集上验证了节能与重建精度提升。
摘要：
在6G时代，随着智能交通系统和数字孪生的引人注目愿景，远程监控有望成为一种普遍实践。海量数据量和频繁更新给无线网络带来了挑战。为应对这些挑战，我们提出了一种基于强化学习的新型智能体驱动生成式语义通信（A-GSC）框架。与现有主要关注语义提取或语义采样的语义通信（SemCom）研究不同，我们通过联合考虑源信息的内在属性和与任务相关的上下文信息，将二者无缝集成。值得注意的是，生成式人工智能（GAI）的引入使得语义编码器和解码器能够独立设计。在这项工作中，我们开发了一种具有跨模态能力的智能体辅助语义编码器，能够跟踪语义变化和信道状况，以执行自适应语义提取和采样。相应地，我们设计了一种兼具预测和生成能力的语义解码器，由两个定制模块组成。此外，所设计模型的有效性已使用UA-DETRAC数据集进行了验证，证明了整体A-GSC框架在节能和重建精度方面的性能提升。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
