Text-based communication is expected to be prevalent in 6G applications such as wireless AI-generated content (AIGC). Motivated by this, this paper addresses the challenges of transmitting text prompts over erasure channels for a text-to-image AIGC task by developing the semantic segmentation and repeated transmission (SMART) algorithm. SMART groups words in text prompts into packets, prioritizing the task-specific significance of semantics within these packets, and optimizes the number of repeated transmissions. Simulation results show that SMART achieves higher similarities in received texts and generated images compared to a character-level packetization baseline, while reducing computing latency by orders of magnitude compared to an exhaustive search baseline.
@article{arxiv.2503.23734,
title = {Semantic Packet Aggregation and Repeated Transmission for Text-to-Image Generation},
author = {Seunghun Lee and Jihong Park and Jinho Choi and Hyuncheol Park},
journal= {arXiv preprint arXiv:2503.23734},
year = {2025}
}