Problem
Research questions and friction points this paper is trying to address.
Semantic Communication
Knowledge-Base
Storage Scalability
Quantization
Storage-Scalable Progressive Semantic Communication via Knowledge-Base Reuse
🤖 AI Summary
本文针对语义通信中知识库存储问题，提出了一种通过重用紧凑知识库集的存储可扩展方法SSKBQ，并引入阶段感知残差监督机制，以解耦传输阶段与维护的知识库数量。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
storage-scalable
knowledge-base reuse
stage-aware residual supervision
progressive refinement
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