ScholarWorks@중앙대학교

초록

The large model sizes and high computational demands of the current semantic communication systems pose challenges for deployment in environments with limited resources. Furthermore, existing semantic communication frameworks predominantly operate unidirectionally, significantly increaseing model redundancy and resource requirements in scenarios requiring simultaneous bidirectional information exchange. To address these challenges, we propose a BidDeepSC-1.58b, integrating three complementary innovations: a unified encoder-decoder architecture enabling bidirectional communication with a halved parameter count, 1.58-bit quantization for extreme model compression, and a slimmable neural network with an intelligent semantic fidelity predictor that dynamically selects optimal width configurations. The experimental results demonstrate that the proposed BidDeepSC-1.58b achieves 88.1% model size reduction (3.79 MB footprint) while outperforming baseline models by over 30% in semantic similarity under challenging channel conditions. The framework reduces inference latency by up to 88% and enables dynamic width adaptation that achieves 39.2% computational savings while maintaining semantic performance thresholds. This system enables the efficient deployment of semantic communication in resource-constrained environments and facilitates bidirectional semantic information exchange across a broad range of applications.

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