Storage-Scalable Progressive Semantic Communication via Knowledge-Base Reuse

Abstract:Existing knowledge-base-assisted semantic communication schemes commonly adopt either single knowledge-base quantization (SKBQ) or multi-knowledge-base residual quantization (MKBQ). SKBQ incurs limited storage overhead but has restricted quantization capacity, whereas MKBQ supports progressive refinement by assigning an independent knowledge base (KB) to each stage, causing the KB storage to grow linearly with the transmission depth. To address this problem, we propose storage-scalable knowledge-base reuse quantization (SSKBQ), which reuses a compact set of KBs across multiple residual refinement stages and thereby decouples the number of transmission stages from the number of maintained KBs. A stage-aware residual supervision mechanism is further introduced to regularize intermediate quantized representations and encourage progressive refinement. Experimental results demonstrate that KB reuse provides an effective solution to the storage scalability problem while maintaining competitive progressive reconstruction performance.

Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI); Image and Video Processing (eess.IV)

arXiv:2609.10112 [cs.LG]

(or arXiv:2609.10112v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.10112 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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