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deep joint source-channel coding empowered two-way relay networks for wireless image transmission

PHYSICAL COMMUNICATION 2025年 68卷

作者： Wang, Xingyu Zheng, Xiangdong Zhang, Lianhong Li, Chao Guangzhou Univ Sch Comp Sci & Cyber Engn Guangzhou 510006 Peoples R China

Compared with the traditional uni-directional relaying, two-way relay networks provide important enhancements and optimizations to modern communication systems. However, with the increasing requirements of artificial intelligence applications for image data transmission, relay-assisted communication technologies are reaching the theoretical limit in terms of bandwidth, which hinders the further development of AI applications. To address this issue, we propose a deep joint source-channel coding empowered two-way relay network (deepJSCC-TWRN) to help image transmission. Specifically, in the deepJSCC-TWRN, a deepJSCC is employed to improve image transmission quality of the TWRN from the perspective of visual semantic information, and each source can achieve optimal performance by being trained in a uniform deep learning framework. For measuring the performance of the proposed deepJSCC-TWRN, we employ the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) as performance metrics. Simulation results show that deepJSCC-TWRN outperforms the baseline method, demonstrating the ability to preserve visual semantic information.

关键词： Two-way relay network Amplify-and-forward relay deep joint source-channel coding Wireless image transmission

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deep joint source-channel coding for Wireless Image Transmission: One-Model-to-Many

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第5期14卷 1501-1505页

作者： Wang, Feng Chen, Xuechen Deng, Xiaoheng Lin, Siyu Cent South Univ Sch Comp Sci & Engn Changsha 410075 Peoples R China Cent South Univ Sch Elect Informat Changsha 410075 Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100091 Peoples R China

In recent years, researches on deep joint source-channel coding (deepJSCC) for wireless communications achieve great success owing to the employment of deep learning (DL). However, all existing deepJSCC schemes only work with a single bandwidth, ignoring the fact that different users have different bandwidths. Under additive Gaussian white noise (AWGN) channel, we present a deepJSCC scheme utilizing auto-regressive model and transformer block for wireless image transmission, where a single model is trained across multiple compression ratios (CRs) through masking, enabling it to adapt to various bandwidth CRs. Specifically, at the transmitter, our encoder first combines convolutional kernels with transformer block to effectively capture both global and local features of an image. Then we transmit a portion of the encoded image by masking. At the receiver, based on the masking strategy, we use auto-regressive model that fully utilizes the known prior information to restore the masked portions of the encoded image. Extensive experiments show that our scheme achieves the adaptation of a single model to various CRs and better image reconstruction quality than the separately models trained under fixed CRs.

关键词： Adaptation models Image restoration Transformers Training Image coding Decoding Bandwidth Image reconstruction Kernel channel coding deep joint source-channel coding random masking auto regressive model

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deep joint source-channel coding for CSI Feedback: An End-to-End Approach

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2023年第1期41卷 260-273页

作者： Xu, Jialong Ai, Bo Wang, Ning Chen, Wei Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Beijing Jiaotong Univ Frontiers Sci Ctr Smart High Speed Railway Syst Beijing 100044 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing Engn Res Ctr High Speed Railway Broadband Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Peng Cheng Lab Res Ctr Networks & Commun Shenzhen 518055 Peoples R China Zhengzhou Univ Henan Joint Int Res Lab Intelligent Networking & Zhengzhou 450001 Peoples R China Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Peoples R China Beijing Jiaotong Univ Key Lab Railway Ind Broadband Mobile Informat Com Beijing 100044 Peoples R China

The increased throughput brought by MIMO technology relies on the knowledge of channel state information (CSI) acquired in the base station (BS). To make the CSI feedback overhead affordable for the evolution of MIMO technology (e.g., massive MIMO and ultra-massive MIMO), deep learning (DL) is introduced to deal with the CSI compression task. In traditional communication systems, the compressed CSI bits is treated equally and expected to be transmitted accurately over the noisy channel. While the errors occur due to the limited bandwidth or low signal-to-noise ratios (SNRs), the reconstruction performance of the CSI degrades drastically. As a branch of semantic communications, deep joint source-channel coding (DJSCC) scheme performs better than the separate source-channel coding (SSCC) scheme-the cornerstone of traditional communication systems-in the limited bandwidth and low SNRs. In this paper, we propose a DJSCC based framework for the CSI feedback task. In particular, the proposed method can simultaneously learn from the CSI source and the wireless channel. Instead of truncating CSI via Fourier transform in the delay domain in existing methods, we apply non-linear transform networks to compress the CSI. Furthermore, we adopt an SNR adaption mechanism to deal with wireless channel variations. The extensive experiments demonstrate the validity, adaptability, and generality of the proposed framework.

关键词： CSI feedback deep joint source-channel coding autoencoder deep learning

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deep joint source-channel coding Over the Relay channel 1

Deep Joint Source-Channel Coding Over the Relay Channel

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1st IEEE International Conference on Machine Learning for Communication and Networking (IEEE ICMLCN)

作者： Bian, Chenghong Shao, Yulin Wu, Haotian Gunduz, Deniz Imperial Coll London Dept Elect & Elect Engn London England Univ Macau State Key Lab Internet Things Smart City Taipa Macao Peoples R China Univ Macau Dept Elect & Comp Engn Taipa Macao Peoples R China

ISBN: (纸本)9798350343205;9798350343199

This paper presents a novel deep joint source-channel coding (deepJSCC) scheme for image transmission over a half-duplex cooperative relay channel. Specifically, we apply deepJSCC to two basic modes of cooperative communications, namely amplify-and-forward (AF) and decode-and-forward (DF). In deepJSCC-AF, the relay simply amplifies and forwards its received signal. In deepJSCC-DF, on the other hand, the relay first reconstructs the transmitted image and then re-encodes it before forwarding. Considering the excessive computation overhead of deepJSCC-DF for recovering the image at the relay, we propose an alternative scheme, called deepJSCC-PF, in which the relay processes and forwards its received signal without necessarily recovering the image. Simulation results show that the proposed deepJSCC-AF, DF, and PF schemes are superior to the digital baselines with BPG compression with polar codes and provide a graceful performance degradation with deteriorating channel quality. Further investigation shows that the PSNR gain of deepJSCC-DF/PF over deepJSCC-AF improves as the channel condition between the source and relay improves. Moreover, the deepJSCC-PF scheme achieves similar performance to deepJSCC-DF with lower computational complexity.

关键词： deep joint source-channel coding cooperative relay networks decode-and-forward

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Process-and-Forward: deep joint source-channel coding Over Cooperative Relay Networks

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2025年第4期43卷 1118-1134页

作者： Bian, Chenghong Shao, Yulin Wu, Haotian Ozfatura, Emre Gunduz, Deniz Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England Univ Macau State Key Lab Internet Things SmartCity Macau Peoples R China Univ Macau Dept Elect & Comp Engn Macau Peoples R China

We introduce deep joint source-channel coding (deepJSCC) schemes for image transmission over cooperative relay channels. The relay either amplifies-and-forwards its received signal, called deepJSCC-AF, or leverages neural networks to extract relevant features from its received signal, called deepJSCC-PF (Process-and-Forward). We consider both half- and full-duplex relays, and propose a novel transformer-based model at the relay. For a half-duplex relay, it is shown that the proposed scheme learns to generate correlated signals at the relay and source to obtain beamforming gains. In the full-duplex case, we introduce a novel block-based transmission strategy, in which the source transmits in blocks, and the relay updates its knowledge about the input signal after each block and generates its own signal. To enhance practicality, a single transformer-based model is used at the relay at each block, together with an adaptive transmission module, which allows the model to seamlessly adapt to different channel qualities and the transmission powers. Simulation results demonstrate the superior performance of deepJSCC-PF compared to the state-of-the-art BPG image compression algorithm operating at the maximum achievable rate of conventional decode-and-forward and compress-and-forward protocols, in both half- and full-duplex relay scenarios over AWGN and Rayleigh fading channels.

关键词： Relays Full-duplex system Half-duplex system Image coding Protocols Transformers Encoding Image reconstruction Decoding Adaptation models deep joint source-channel coding cooperative relay networks decode-and-forward deep learning-based transceiver design

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Visually Secure deep joint source-channel coding With Chaotic Map Against deep Known-Plaintext Attack

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2025年 6卷 1847-1858页

作者： Fu, Yuyang Suto, Katsuya Univ Electrocommun Grad Sch Informat & Engn Tokyo 1828585 Japan

In recent years, deep learning-based joint source-channel coding (DJSCC) has gained significant attention for its impressive performance in image transmission. Unlike traditional separate source-channel coding (SSCC) methods, DJSCC performs particularly well in low signal-to-noise ratio (SNR) and limited bandwidth environments. However, ensuring the security of private information during transmission remains a critical concern. A notable limitation of DJSCC is its incompatibility with traditional encryption methods used for secure communications, making it vulnerable to eavesdropping attacks. To address this issue, we propose integrating a chaotic map encryption method into the DJSCC framework for secure wireless image transmission. This approach leverages chaotic sequence to shuffle the position of the elements in latent space without altering the values of the latent tensor. This allows the encryption process to be designed independently of DJSCC, eliminating the need for re-training the end-to-end model. Our proposed method preserves DJSCC's superior transmission characteristics, ensuring high-quality image reconstruction at the receiver, while effectively ensuring the security against deep learning-based known plaintext attacks (deep KPA).

关键词： Encryption Chaotic communication Image reconstruction Decoding Receivers Tensors Image communication channel coding Image coding Encoding Chaotic map encryption deep joint source-channel coding image transmission known-plaintext attack visual security

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deep joint source-channel coding for Multi-Task Network

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IEEE SIGNAL PROCESSING LETTERS 2021年 28卷 1973-1977页

作者： Wang, Mengyang Zhang, Zhicong Li, Jiahui Ma, Mengyao Fan, Xiaopeng Harbin Inst Technol Sch Comp Sci Harbin 150001 Peoples R China Huawei Wireless Technol Lab Shenzhen 518129 Peoples R China

Multi-task learning (MTL) is an efficient way to improve the performance of related tasks by sharing knowledge. However, most existing MTL networks run on a single end and are not suitable for collaborative intelligence (CI) scenarios. In this work, we propose an MTL network with a deep joint source-channel coding (JSCC) framework, which allows operating under CI scenarios. We first propose a feature fusion based MTL network (FFMNet) for joint object detection and semantic segmentation. Compared with other MTL networks, FFMNet gets higher performance with fewer parameters. Then FFMNet is split into two parts, which run on a mobile device and an edge server respectively. The feature generated by the mobile device is transmitted through the wireless channel to the edge server. To reduce the transmission overhead of the intermediate feature, a deep JSCC network is designed. By combining two networks together, the whole model achieves 512x compression for the intermediate feature and a performance loss within 2% on both tasks. At last, by training with noise, the FFMNet with JSCC is robust to various channel conditions and outperforms the separate source and channel coding scheme.

关键词： Feature extraction Task analysis Servers Semantics Object detection Training Mobile handsets Collaborative intelligence multi-task learning deep joint source-channel coding

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Constellation Design for deep joint source-channel coding

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IEEE SIGNAL PROCESSING LETTERS 2022年 29卷 1442-1446页

作者： Wang, Mengyang Li, Jiahui Ma, Mengyao Fan, Xiaopeng Harbin Inst Technol Sch Comp Sci Harbin 150001 Peoples R China Huawei Wireless Technol Lab Shenzhen 518129 Peoples R China

deep learning-based joint source-channel coding (JSCC) has shown excellent performance in image and feature transmission. However, the output values of the JSCC encoder are continuous, which makes the constellation of modulation complex and dense. It is hard and expensive to design radio frequency chains for transmitting such full-resolution constellation points. In this paper, two methods of mapping the full-resolution constellation to finite constellation are proposed for real system implementation. The constellation mapping results of the proposed methods correspond to regular constellation and irregular constellation, respectively. We apply the methods to existing deep JSCC models and evaluate them on AWGN channels with different signal-to-noise ratios (SNRs). Experimental results show that the proposed methods outperform the traditional uniform quadrature amplitude modulation (QAM) constellation mapping method by only adding a few additional parameters.

关键词： Quadrature amplitude modulation Quantization (signal) channel coding Receivers Computational modeling Clustering algorithms Backpropagation Constellation design constellation mapping methods deep joint source-channel coding

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Lightweight deep joint source-channel coding for Gauss-Markov sources over AWGN channel

Lightweight Deep Joint Source-Channel Coding for Gauss-Marko...

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IEEE Wireless Communications and Networking Conference (WCNC)

作者： Li, Yishen Chen, Xuechen Deng, Xiaoheng Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China

ISBN: (纸本)9781665491228

In this paper, we study the design of neural network based joint source-channel coding (JSCC) for point-to-point communication of Gauss-Markov sources over the additive white Gaussian noise (AWGN) channel with bandwidth compression. Among the existing deep learning (DL) -based JSCC methods for such sources, the long short-term memory (LSTM) based structure has good performance. However, it takes up huge time and space consumption because of its complex structure. In this work, we propose to adopt the causal convolution and dilated convolution to form our encoder and decoder due to their abilities of effectively extracting the temporal information of sources and their superiority in terms of reducing the time and space consumption. Experimental results show that the proposed model outperforms the traditional JSCC schemes and is comparable to the LSTM-based model in terms of source reconstruction quality. Besides, the proposed model shows a great robustness in the case of channel quality mismatch and correlation coefficient mismatch. Furthermore, our model takes lower time in the test phase and much lower space consumption compared to LSTM-based model.

关键词： deep joint source-channel coding causal convolution dilated convolution Gauss-Markov source bandwidth compression

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Adaptive deep joint source-channel coding for One-to-Many Wireless Image Transmission

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IEEE TRANSACTIONS ON BROADCASTING 2025年

作者： Luo, Lei He, Ziyang Wu, Junjie Guo, Hongwei Zhu, Ce Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 610051 Peoples R China Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China Honghe Univ Mengzi 661100 Peoples R China Univ Elect Sci & Technol China Glasgow Coll Chengdu 610051 Peoples R China

deep learning based joint source-channel coding (DJSCC) has recently made significant progress and emerged as a potential solution for future wireless communication. However, there are still several crucial issues that necessitate further in-depth exploration to enhance the efficiency of DJSCC, such as channel quality adaptability, bandwidth adaptability, and the delicate balance between efficiency and complexity. This work proposes an adaptive deep joint source-channel coding scheme tailored for one-to-many wireless image transmission scenarios (ADMIT). First, to effectively improve transmission performance, neighboring attention is introduced as the backbone for the proposed ADMIT method. Second, a channel quality adaptive module (CQAM) is designed based on multi-scale feature fusion, which seamlessly adapts to fluctuating channel conditions across a wide range of channel signal-to-noise ratios (CSNRs). Third, to be precisely tailored to different bandwidth resources, the channel gained adaptive module (CGAM) dynamically adjusts the significance of individual channels within the latent space, which ensures seamless varying bandwidth accommodation with a single model through bandwidth adaptation and symbol completion. Additionally, to mitigate the imbalance of loss across multiple bandwidth ratios during the training process, the gradient normalization (GradNorm) based training strategy is leveraged to ensure adaptive balancing of loss decreasing. The extensive experimental results demonstrate that the proposed method significantly enhances transmission performance while maintaining relatively low computational complexity. The source codes are available at: https://***/llsurreal919/ADMIT.

关键词： deep joint source-channel coding bandwidth adaptability neighboring attention gradient normalization

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