Semantic Feature Channel Optimization: A Unified Framework for Analog and Digital Semantic Communications

Abstract

Semantic communication (SC) aims to improve communication efficiency by transmitting only task-relevant information, termed semantic features (SFs). Most existing SC frameworks achieve this by optimizing the encoder and decoder while treating the channel between them as fixed. In this paper, we introduce a new perspective by explicitly modeling the entire process from the encoder output to the decoder input as an SF channel, resulting in an encoder-SF channel-decoder pipeline. We observe that the SF channel is configurable through transceiver operations, such as power allocation, thereby providing an additional degree of freedom for improving task performance. Inspired by this, we formulate a joint optimization problem for the encoder, SF channel, and decoder under a mutual information constraint between the transmitted and reconstructed SFs. To provide analytical insight, we derive the optimal SF channel in closed form for an analytically tractable setting. Based on this formulation, we develop a unified semantic feature channel optimization framework applicable to both analog and digital SC systems. To realize the SF channel in general communication systems, we further propose a physical-layer calibration strategy that aligns the actual SF channel with the trained one. Simulation results demonstrate that the proposed framework consistently improves task performance across various communication environments.