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.

翻译：在6G时代，随着智能交通系统与数字孪生等愿景的推进，远程监控有望成为普遍应用。海量数据与频繁更新对无线网络构成了挑战。为应对这些挑战，本文提出一种基于强化学习的智能体驱动生成式语义通信（A-GSC）新框架。与现有语义通信研究主要聚焦于语义提取或语义采样不同，本框架通过联合考虑源信息的内在属性与任务相关的上下文信息，将二者无缝集成。值得注意的是，生成式人工智能的引入使得语义编码器与解码器能够独立设计。本文开发了一种具备跨模态能力的智能体辅助语义编码器，能够追踪语义变化与信道状态，实现自适应的语义提取与采样。相应地，我们设计了一个兼具预测与生成能力的语义解码器，包含两个定制化模块。此外，所设计模型的有效性已在UA-DETRAC数据集上得到验证，证明了整体A-GSC框架在节能与重建精度方面的性能提升。