SAFE: Semantic Adaptive Feature Extraction with Rate Control for 6G Wireless Communications

TL;DR

The paper introduces SAFE, a semantic adaptive feature extraction framework for 6G wireless communications that enhances bandwidth efficiency by adapting semantic transmission to varying channel conditions using advanced learning algorithms.

Contribution

It presents a novel adaptive framework and learning algorithms that improve semantic communication efficiency across different bandwidth conditions.

Findings

Effective adaptive semantic extraction demonstrated in simulations

Significant bandwidth efficiency improvements shown

Quality evaluations confirm robustness of the approach

Abstract

Most current Deep Learning-based Semantic Communication (DeepSC) systems are designed and trained exclusively for particular single-channel conditions, which restricts their adaptability and overall bandwidth utilization. To address this, we propose an innovative Semantic Adaptive Feature Extraction (SAFE) framework, which significantly improves bandwidth efficiency by allowing users to select different sub-semantic combinations based on their channel conditions. This paper also introduces three advanced learning algorithms to optimize the performance of SAFE framework as a whole. Through a series of simulation experiments, we demonstrate that the SAFE framework can effectively and adaptively extract and transmit semantics under different channel bandwidth conditions, of which effectiveness is verified through objective and subjective quality evaluations.