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

Hyuncheol Park; Jihong Park; Jinho Choi; Seunghun Lee

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

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arxiv 2503.23734 v1 pith:BYUUM3AY submitted 2025-03-31 eess.SP

Seunghun Lee , Jihong Park , Jinho Choi , Hyuncheol Park This is my paper · ORCID

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keywords repeatedsmartaigcbaselinecomparedpacketspromptssemantic

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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.

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Low-Complexity Semantic Packet Aggregation for Token Communication via Lookahead Search
eess.SP 2025-06 conditional novelty 5.0 of 10

SemPA-Look groups tokens into packets using a leave-one-out residual semantic score and a fixed-width lookahead search, matching near-optimal ATS at linear text-encoding complexity.

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