Fundamental Limitation of Semantic Communications: Neural Estimation for Rate-Distortion

• URL: http://arxiv.org/abs/2401.01176v1
• Date: Tue, 2 Jan 2024 12:10:16 GMT
• Title: Fundamental Limitation of Semantic Communications: Neural Estimation for Rate-Distortion
• Authors: Dongxu Li, Jianhao Huang, Chuan Huang, Xiaoqi Qin, Han Zhang, and Ping Zhang
• Abstract summary: This paper studies the fundamental limit of semantic communications over the discrete memoryless channel. We adopt the semantic rate-distortion function (SRDF) to study the relationship among the minimum compression rate, observation distortion, semantic distortion, and channel capacity.
• Score: 19.615466425874402
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper studies the fundamental limit of semantic communications over the discrete memoryless channel. We consider the scenario to send a semantic source consisting of an observation state and its corresponding semantic state, both of which are recovered at the receiver. To derive the performance limitation, we adopt the semantic rate-distortion function (SRDF) to study the relationship among the minimum compression rate, observation distortion, semantic distortion, and channel capacity. For the case with unknown semantic source distribution, while only a set of the source samples is available, we propose a neural-network-based method by leveraging the generative networks to learn the semantic source distribution. Furthermore, for a special case where the semantic state is a deterministic function of the observation, we design a cascade neural network to estimate the SRDF. For the case with perfectly known semantic source distribution, we propose a general Blahut-Arimoto algorithm to effectively compute the SRDF. Finally, experimental results validate our proposed algorithms for the scenarios with ideal Gaussian semantic source and some practical datasets.

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