# Semantic Packet Aggregation and Repeated Transmission for Text-to-Image Generation

Source: [Semantic Packet Aggregation and Repeated Transmission for Text-to-Image Generation](<https://www.sota2.com/research/paper/68fa804f1e209131cea9ca40>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T06:59:24.854Z

Authors: Seunghun Lee, Jihong Park, Jinho Choi, Hyuncheol Park

Published: 2025-03-31T05:14:40.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2503.23734>)

[PDF](<https://arxiv.org/pdf/2503.23734.pdf>)