Recent works have shown that joint source-channel coding (JSCC) schemes using
deep neural networks (DNNs), called DeepJSCC, provide promising results in
wireless image transmission. However, these methods mostly focus on the
distortion of the reconstructed signals with respect to the input image, rather
than their perception by humans. However, focusing on traditional distortion
metrics alone does not necessarily result in high perceptual quality,
especially in extreme physical conditions, such as very low bandwidth
compression ratio (BCR) and low signal-to-noise ratio (SNR) regimes. In this
work, we propose two novel JSCC schemes that leverage the perceptual quality of
deep generative models (DGMs) for wireless image transmission, namely
InverseJSCC and GenerativeJSCC. While the former is an inverse problem approach
to DeepJSCC, the latter is an end-to-end optimized JSCC scheme. In both, we
optimize a weighted sum of mean squared error (MSE) and learned perceptual
image patch similarity (LPIPS) losses, which capture more semantic similarities
than other distortion metrics. InverseJSCC performs denoising on the distorted
reconstructions of a DeepJSCC model by solving an inverse optimization problem
using style-based generative adversarial network (StyleGAN). Our simulation
results show that InverseJSCC significantly improves the state-of-the-art
(SotA) DeepJSCC in terms of perceptual quality in edge cases. In
GenerativeJSCC, we carry out end-to-end training of an encoder and a
StyleGAN-based decoder, and show that GenerativeJSCC significantly outperforms
DeepJSCC both in terms of distortion and perceptual quality.

本文提出了两种基于深度生成 (deng) 模型的联合源信号编码方案，分别是 InverseJSCC 和 GenerativeJSCC。通过介绍一种基于修改 DeepJSCC 的逆问题方法和一种端到 dalou 训练的 JSCC 方法，本文显示了这些方法在视觉感知质量方面的改进。

语义图像传输的生成联合源 - 信道编码

Generative Joint Source-Channel Coding for Semantic Image Transmission

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。

离散时间模拟传输下的语义通信：PAPR 视角

Semantic Communications with Discrete-time Analog Transmission: A PAPR  Perspective

We consider wireless transmission of images in the presence of channel output
feedback. From a Shannon theoretic perspective feedback does not improve the
asymptotic end-to-end performance, and separate source coding followed by
capacity-achieving channel coding, which ignores the feedback signal, achieves
the optimal performance. It is well known that separation is not optimal in the
practical finite blocklength regime; however, there are no known practical
joint source-channel coding (JSCC) schemes that can exploit the feedback signal
and surpass the performance of separation-based schemes. Inspired by the recent
success of deep learning methods for JSCC, we investigate how noiseless or
noisy channel output feedback can be incorporated into the transmission system
to improve the reconstruction quality at the receiver. We introduce an
autoencoder-based JSCC scheme, which we call DeepJSCC-f, that exploits the
channel output feedback, and provides considerable improvements in terms of the
end-to-end reconstruction quality for fixed-length transmission, or in terms of
the average delay for variable-length transmission. To the best of our
knowledge, this is the first practical JSCC scheme that can fully exploit
channel output feedback, demonstrating yet another setting in which modern
machine learning techniques can enable the design of new and efficient
communication methods that surpass the performance of traditional structured
coding-based designs.

通过自编码机（autoencoder）我们提出了一种新的 JSCC 方案，将无噪声或有噪声的信道输出反馈引入到传输系统中，以提高接收端的重建质量或平均延迟，并展示了现代机器学习技术在通信领域中的应用。