Abstract
This study proposes a separate source-channel coding (SSCC) framework to address semantic communication challenges in MIMO systems, overcoming the limitations of joint source-channel coding (JSCC) in channel adaptation and model reusability. Traditional systems suffer from bit-level redundancy in 6G, while JSCC struggles with complex channel variations. Our solution decouples semantic processing from channel coding through a three-tier architecture: (1) Variational autoencoder (VAE)-based semantic encoder and decoder for source coding, (2) A communication-informed bottleneck attribution (CIBA) mechanism quantifying feature importance for learning tasks, and (3) An importance-aware resource allocation scheme aligning communication objectives with deep learning tasks. Systematic experiments validate CIBA's effectiveness in deriving importance scores that bridge learning tasks and communication optimization. Comparisons of feature perturbation schemes confirm the necessity of importance-aware resource allocation, with the proposed allocation strategy outperforming conventional methods in task performance metrics. The SSCC design enhances model reusability while maintaining adaptability to diverse MIMO configurations. By integrating interpretable AI with resource management, this work establishes a foundation for SSCC semantic communication systems in resource-constrained environments, prioritizing semantic fidelity and task efficacy over bit-level redundancy. The methodology highlights the critical role of importance awareness in optimizing both communication efficiency and learning task performance.