On the Rate-Distortion-Complexity Tradeoff for Semantic Communication

• URL: http://arxiv.org/abs/2602.14481v1
• Date: Mon, 16 Feb 2026 05:45:52 GMT
• Title: On the Rate-Distortion-Complexity Tradeoff for Semantic Communication
• Authors: Jingxuan Chai, Yong Xiao, Guangming Shi,
• Abstract summary: This paper proposes a rate-distortion-complexity (RDC) framework which extends the classical rate-distortion theory.<n>We derive the theoretical results of the minimum achievable rate under given constraints on semantic distance and complexity.<n>Our results show a fundamental three-way tradeoff among achievable rate, semantic distance, and model complexity.
• Score: 42.300429885256435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic communication is a novel communication paradigm that focuses on conveying the user's intended meaning rather than the bit-wise transmission of source signals. One of the key challenges is to effectively represent and extract the semantic meaning of any given source signals. While deep learning (DL)-based solutions have shown promising results in extracting implicit semantic information from a wide range of sources, existing work often overlooks the high computational complexity inherent in both model training and inference for the DL-based encoder and decoder. To bridge this gap, this paper proposes a rate-distortion-complexity (RDC) framework which extends the classical rate-distortion theory by incorporating the constraints on semantic distance, including both the traditional bit-wise distortion metric and statistical difference-based divergence metric, and complexity measure, adopted from the theory of minimum description length and information bottleneck. We derive the closed-form theoretical results of the minimum achievable rate under given constraints on semantic distance and complexity for both Gaussian and binary semantic sources. Our theoretical results show a fundamental three-way tradeoff among achievable rate, semantic distance, and model complexity. Extensive experiments on real-world image and video datasets validate this tradeoff and further demonstrate that our information-theoretic complexity measure effectively correlates with practical computational costs, guiding efficient system design in resource-constrained scenarios.

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