Recent progress in deep learning (DL)-based joint source-channel coding
(DeepJSCC) has led to a new paradigm of semantic communications. Two salient
features of DeepJSCC-based semantic communications are the exploitation of
semantic-aware features directly from the source signal, and the discrete-time
analog transmission (DTAT) of these features. Compared with traditional digital
communications, semantic communications with DeepJSCC provide superior
reconstruction performance at the receiver and graceful degradation with
diminishing channel quality, but also exhibit a large peak-to-average power
ratio (PAPR) in the transmitted signal. An open question has been whether the
gains of DeepJSCC come from the additional freedom brought by the high-PAPR
continuous-amplitude signal. In this paper, we address this question by
exploring three PAPR reduction techniques in the application of image
transmission. We confirm that the superior image reconstruction performance of
DeepJSCC-based semantic communications can be retained while the transmitted
PAPR is suppressed to an acceptable level. This observation is an important
step towards the implementation of DeepJSCC in practical semantic communication
systems.

本文针对深度学习 (DL) 的基于联合源信道编码 (DeepJSCC) 的最新进展进行了研究，探讨了在图像传输中通过三种 PAPR 降低技术来抑制高峰均比功率 (PAPR) 的传输信号，实现亮点提取和传输。通过实验结果，证实了 DeepJSCC 语义通信的优越的重构性能可以在传输 PAPR 被抑制到可接受水平时保持不变。