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Deep Semantic-Aware SCMA Codebook Learning for Semantic Communication Systems
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DOI：10.1109/lwc.2026.3709330.png)
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Abstract
En 中文
Sparse code multiple access (SCMA) has emerged as a promising non-orthogonal multiple access (NOMA) technique for future wireless communications. However, conventional SCMA is inherently designed for bit-level transmission, which ignores semantic-level information and lacks semantic-aware codebook construction capabilities. To address this limitation, this letter proposes a semantic-aware SCMA codebook learning framework, named SCMA-SC, which jointly optimizes semantic representation and sparse codeword generation through an end-to-end trainable architecture. By integrating semantic communications with SCMA, the proposed framework enables semantic-aware overloaded transmission over limited wireless resources. Simulation results demonstrate that SCMA-SC outperforms both conventional SCMA and power-domain NOMA semantic communication systems in terms of semantic reconstruction quality and image transmission performance.
Keywords:
Sparse code multiple access
codebook learning framework
semantic communication
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5.5
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663
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0