论文标题
带有离散时间模拟传输的语义通信：PAPR透视图
Semantic Communications with Discrete-time Analog Transmission: A PAPR Perspective
论文作者
论文摘要
基于深度学习（DL）的联合源通道编码（DEEPJSCC）的最新进展导致了语义通信的新范式。基于DEEPJSCC的语义通信的两个显着特征是直接从源信号中对语义感知功能的开发，以及这些功能的离散时间模拟传输（DTAT）。与传统的数字通信相比，与DEEPJSCC的语义通信在接收器上提供了出色的重建性能，并具有优美的降级，并且频道质量降低，但在传输信号中也表现出很大的峰值功率比（PAPR）。一个空旷的问题是，DeepJSCC的收益是否来自高PAPR连续振幅信号带来的额外自由。在本文中，我们通过在应用图像传输的应用中探索三种PAPR还原技术来解决这个问题。我们确认，在将传输的PAPR抑制至可接受的水平时，可以保留基于DEEPJSCC的语义通信的出色图像重建性能。该观察结果是在实用语义通信系统中实施DEEPJSCC的重要一步。
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.
