Abstract
In the Internet of Vehicles (IoV), data-intensive
sensing and perception tasks require the transmission of large
amounts of information, while available bandwidth of the whole
system is often limited. To address this issue, this paper proposes
a semantic-driven Communication framework which prioritizes
task-relevant semantic information and transmitting semantic
labels for contextual regions, significantly reducing bandwidth
consumption while preserving semantic integrity. At the receiver,
a GAN-based reconstruction module is further designed to
recover contextual information from the received semantic cues,
ensuring that task-related semantics are preserved. Experimental
results show that the proposed framework can effectively reduce
bandwidth consumption while maintaining high semantic reconstruction quality and strong robustness, particularly under low
signal to interference plus noise ratio (SINR) conditions.
Index Terms—Internet of Vehicles, bandwidth-limited communications, semantic communication, generative adversarial
networks, low-SINR condition