Agent-driven Generative Semantic Communication for Remote Surveillance
Wanting Yang Zehui Xiong Yanli Yuan Wenchao Jiang Tony Q. S. Quek Merouane Debbah
In the era of 6G, featuring compelling visions of intelligent transportation
system, digital twins, remote surveillance is poised to become a ubiquitous
practice. The substantial data volume and frequent updates present challenges
in wireless networks. To address this, 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 semantic compression or semantic sampling, we
seamlessly cascade both together by jointly considering the intrinsic
attributes of source information and the contextual information regarding the
task. Notably, the introduction of the generative artificial intelligence (GAI)
enables the independent design of semantic encoders and decoders. In this work,
we develop an agent-assisted semantic encoder leveraging the knowledge based
soft actor-critic algorithm, which can track the semantic changes, channel
condition, and sampling intervals, so as to perform adaptive semantic sampling.
Accordingly, we design a semantic decoder with both predictive and generative
capabilities, which consists of two tailored modules. Moreover, the
effectiveness of the designed models has been verified based on the dataset
generated from CDNet2014, and the performance gain of the overall A-GSC
framework in both energy saving and reconstruction accuracy have been
demonstrated.