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joint source-channel coding for channel-Adaptive Digital Semantic Communications

IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2025年第1期11卷 75-89页

作者： Park, Joohyuk Oh, Yongjeong Kim, Seonjung Jeon, Yo-Seb POSTECH Dept Elect Engn Pohang 37673 Gyeongbuk South Korea

In this paper, we propose a novel joint source-channel coding (JSCC) approach for channel-adaptive digital semantic communications. In semantic communication systems with digital modulation and demodulation, robust design of JSCC encoder and decoder becomes challenging not only due to the unpredictable dynamics of channel conditions but also due to diverse modulation orders. To address this challenge, we first develop a new demodulation method which assesses the uncertainty of the demodulation output to improve the robustness of the digital semantic communication system. We then devise a robust training strategy which enhances the robustness and flexibility of the JSCC encoder and decoder against diverse channel conditions and modulation orders. To this end, we model the relationship between the encoder's output and decoder's input using binary symmetric erasure channels and then sample the parameters of these channels from diverse distributions. We also develop a channel-adaptive modulation technique for an inference phase, in order to reduce the communication latency while maintaining task performance. In this technique, we adaptively determine modulation orders for the latent variables based on channel conditions. Using simulations, we demonstrate the superior performance of the proposed JSCC approach for image classification, reconstruction, and retrieval tasks compared to existing JSCC approaches.

关键词： Semantics Modulation Demodulation Training Task analysis Decoding Communication systems joint source-channel coding semantic communications task-oriented communications end-to-end training channel-adaptive modulation

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Rate-Distortion-Perception Controllable joint source-channel coding for High-Fidelity Generative Semantic Communications

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2025年第2期11卷 672-686页

作者： Tan, Kailin Dai, Jincheng Liu, Zhenyu Wang, Sixian Qin, Xiaoqi Xu, Wenjun Niu, Kai Zhang, Ping Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Beijing 100876 Peoples R China Univ Surrey Inst Commun Syst 5GIC Guildford GU2 7XH England Univ Surrey Inst Commun Syst 6GIC Guildford GU2 7XH England Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

End-to-end image transmission has recently become a crucial trend in intelligent wireless communications, driven by the increasing demand for high bandwidth efficiency. However, existing methods primarily optimize the trade-off between bandwidth cost and objective distortion, often failing to deliver visually pleasing results aligned with human perception. In this paper, we propose a novel rate-distortion-perception (RDP) jointly optimized joint source-channel coding (JSCC) framework to enhance perception quality in human communications. Our RDP-JSCC framework integrates a flexible plug-in conditional Generative Adversarial Networks (GANs) to provide detailed and realistic image reconstructions at the receiver, overcoming the limitations of traditional rate-distortion optimized solutions that typically produce blurry or poorly textured images. Based on this framework, we introduce a distortion-perception controllable transmission (DPCT) model, which addresses the variation in the perception-distortion trade-off. DPCT uses a lightweight spatial realism embedding module (SREM) to condition the generator on a realism map, enabling the customization of appearance realism for each image region at the receiver from a single transmission. Furthermore, for scenarios with scarce bandwidth, we propose an interest-oriented content-controllable transmission (CCT) model. CCT prioritizes the transmission of regions that attract user attention and generates other regions from an instance label map, ensuring both content consistency and appearance realism for all regions while proportionally reducing channel bandwidth costs. Comprehensive experiments demonstrate the superiority of our RDP-optimized image transmission framework over state-of-the-art engineered image transmission systems and advanced perceptual methods.

关键词： Image reconstruction Bandwidth Entropy Wireless communication Optimization Nonlinear distortion Generative adversarial networks Transforms Receivers Image coding joint source-channel coding RDP optimization generative adversarial networks human perception

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SwinJSCC: Taming Swin Transformer for Deep joint source-channel coding

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2025年第1期11卷 90-104页

作者： Yang, Ke Wang, Sixian Dai, Jincheng Qin, Xiaoqi Niu, Kai Zhang, Ping Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

As one of the key techniques to realize semantic communications, end-to-end optimized neural joint source-channel coding (JSCC) has made great progress over the past few years. A general trend in many recent works pushing the model adaptability or the application diversity of neural JSCC is based on the convolutional neural network (CNN) backbone, whose model capacity is yet limited, inherently leading to inferior system coding gain against traditional coded transmission systems. In this paper, we establish a new neural JSCC backbone that can also adapt flexibly to diverse channel conditions and transmission rates within a single model, our open-source project aims to promote the research in this field. Specifically, we show that with elaborate design, neural JSCC codec built on the emerging Swin Transformer backbone achieves superior performance than conventional neural JSCC codecs built upon CNN, while also requiring lower end-to-end processing latency. Paired with two spatial modulation modules that scale latent representations based on the channel state information and target transmission rate, our baseline SwinJSCC can further upgrade to a versatile version, which increases its capability to adapt to diverse channel conditions and rate configurations. Extensive experimental results show that our SwinJSCC achieves better or comparable performance versus the state-of-the-art engineered BPG + 5G LDPC coded transmission system with much faster end-to-end coding speed, especially for high-resolution images, in which case traditional CNN-based JSCC yet falls behind due to its limited model capacity.

关键词： joint source-channel coding swin transformer attention mechanism image communications

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SNR-EQ-JSCC: joint source-channel coding With SNR-Based Embedding and Query

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第3期14卷 881-885页

作者： Zhang, Hongwei Tao, Meixia Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr Shanghai 200240 Peoples R China

Coping with the impact of dynamic channels is a critical issue in joint source-channel coding (JSCC)-based semantic communication systems. In this letter, we propose a lightweight channel-adaptive semantic coding architecture called SNR-EQ-JSCC. It is built upon the generic Transformer model and achieves channel adaptation (CA) by Embedding the signal-to-noise ratio (SNR) into the attention blocks and dynamically adjusting attention scores through channel-adaptive Queries. Meanwhile, penalty terms are introduced in the loss function to stabilize the training process. Considering that instantaneous SNR feedback may be imperfect, we propose an alternative method that uses only the average SNR, which requires no retraining of SNR-EQ-JSCC. Simulation results conducted on image transmission demonstrate that the proposed SNR-EQ-JSCC outperforms the state-of-the-art SwinJSCC in peak signal-to-noise ratio (PSNR) and perception metrics while only requiring 0.05% of the storage overhead and 6.38% of the computational complexity for CA. Moreover, the channel-adaptive query method demonstrates significant improvements in perception metrics. When instantaneous SNR feedback is imperfect, SNR-EQ-JSCC using only the average SNR still surpasses baseline schemes.

关键词： Signal to noise ratio Transformers Image coding Encoding Training Fading channels Semantic communication Receivers Merging Attention mechanisms Semantic communications channel adaptation multi-head attention joint source-channel coding

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Deep joint source-channel coding Using FFT-Enabled Convolutional Neural Network for Image Transmission 22

Deep Joint Source-Channel Coding Using FFT-Enabled Convoluti...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Mori, Tomoka Hisano, Daisuke Graduate School of Engineering Osaka University Osaka Japan

ISBN: (纸本)9798331508050

Deep joint source-channel coding (DeepJSCC) has attracted attention as a type of semantic communication that shares not only information but also meaning and intent, and it is a type of deep learning that uses an autoencoder instead of conventional source and channel coding. DeepJSCC with convolutional neural network (CNN) layers has been studied specifically for image transmission, where it avoids cliff effects and achieves higher image quality even in the low signal-to-noise power ratio (SNR) range. In addition, DeepJSCC is suit for services related to the Internet of things (IoT), and is likely to be widely applicable to monitoring-related applications such as surveillance cameras. For this reason, it may not be possible to provide sufficient GPU resources, especially for the transmitter where the encoder is installed. Therefore, we propose a fast Fourier transform (FFT)-based DeepJSCC that replaces CNN with FFT and element-wise product to reduce the computation time of DeepJSCC. This paper evaluates the impact of changing the layer structure on image quality. © 2025 IEEE.

关键词： deep learning Image coding joint source-channel coding

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joint source-channel coding FOR RASTER DOCUMENT TRANSMISSION OVER MOBILE RADIO

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IEE PROCEEDINGS-I COMMUNICATIONS SPEECH AND VISION 1989年第6期136卷 375-380页

作者： WYRWAS, RR FARRELL, PG Department of Electrical Engineering Manchester Communications Research Group University of Manchester Manchester UK

coding schemes for binary raster document transmission in the mobile radio environment are explored. For many applications, standard facsimile is too costly in terms of capital investment and transmission time. In this paper we present joint source-channel coding as an alternative, cost-effective solution for low resolution graphics transmission. We present SEA-RL coding as a novel and useful example of joint source-channel coding.

关键词： low resolution graphics transmission Mobile radio systems data compression Facsimile transmission mobile radio systems encoding run-length coding mobile radio SEA-RL coding joint source-channel coding Codes cost-effective facsimile binary raster document transmission

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joint source-channel coding for wireless object-based video communications utilizing data hiding

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2006年第8期15卷 2158-2169页

作者： Wang, Haohong Tsaftaris, Sotirios A. Katsaggelos, Aggelos K. Marvell Technol Grp Santa Clara CA 95054 USA Northwestern Univ Dept Elect & Comp Engn Evanston IL 60208 USA

In recent years, joint source-channel coding for multimedia communications has gained increased popularity. However, very limited work has been conducted to address the problem of joint source-channel coding for object-based video. In this paper, we propose a data hiding scheme that improves the error resilience of object-based video by adaptively embedding the shape and motion information into the texture data. Within a rate-distortion theoretical framework, the source coding, channel coding, data embedding, and decoder error concealment are jointly optimized based on knowledge of the transmission channel conditions. Our goal is to achieve the best video quality as expressed by the minimum total expected distortion. The optimization problem is solved using Lagrangian relaxation and dynamic programming. The performance of the proposed scheme is tested using simulations of a Rayleigh-fading wireless channel, and the algorithm is implemented based on the MPEG-4 verification model. Experimental results indicate that the proposed hybrid source-channel coding scheme significantly outperforms methods without data hiding or unequal error protection.

关键词： data embedding data hiding joint source-channel coding MPEG-4 standard object-based video rate-distortion unequal error protection (UEP) video coding video communications wireless channel

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joint source-channel coding Over Additive Noise Analog channels Using Mixture of Variational Autoencoders

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2021年第7期39卷 2000-2013页

作者： Saidutta, Yashas Malur Abdi, Afshin Fekri, Faramarz Geogia Inst Technol Dept Elect & Comp Engn Atlanta GA 30318 USA Qualcomm Technol Inc San Diego CA 92121 USA

In this paper, we present a learning scheme for joint source-channel coding (JSCC) over analog independent additive noise channels. We formulate the learning problem by showing that the minimization loss function from rate-distortion theory, is upper bounded by the loss function of the Variational Autoencoder (VAE). We show that when the source dimension is greater than the channel dimension, the encoding of two source samples in the neighborhood of each other need not be near each other. Such discontinuous projection needs to be accounted for by using multiple encoders and selecting an encoder to encode samples on a particular side of the discontinuity. We explore two selection methodologies, one based on an intuitive rule and the other where it is posed as a learning task in a Mixture-of-Experts (MoE) setup. We analyze the gradients of these methods and reason why the latter is better at avoiding local optima. We show the efficacy of the proposed methodology by simulating the performance of the system for JSCC of Gaussian sources over AWGN channels and showing that the learned solutions are close to or better than the ones proposed earlier. The proposed methodology is also naturally capable of generalizing to other source distributions which we showcase by simulating for Laplace sources. The learned systems are also robust to changes in channel conditions. Further, a single system can be trained to generalize over a range of channel conditions provided the channel conditions are known at both the transmitter and the receiver. Finally, we evaluate our proposed methodology on three different image datasets and showcase consistent improvement over existing methods due to the VAE formulation.

关键词： Decoding Training AWGN channels Neural networks channel coding Scalability Optimization joint source-channel coding machine learning deep learning Variational Autoencoders

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joint source-channel coding Excess Distortion Exponent for Some Memoryless Continuous-Alphabet Systems

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IEEE TRANSACTIONS ON INFORMATION THEORY 2009年第3期55卷 1296-1319页

作者： Zhong, Yangfan Alajaji, Fady Campbell, L. Lorne Queens Univ Dept Math & Stat Kingston ON K7L 3N6 Canada

We investigate the joint source-channel coding (JSCC) excess distortion exponent E-J (the exponent of the probability of exceeding a prescribed distortion level) for some memoryless communication systems with continuous alphabets. We first establish upper and lower bounds for E-J for systems consisting of a memoryless Gaussian source under the squared-error distortion fidelity criterion and a memoryless additive Gaussian noise channel with a quadratic power constraint at the channel input. A necessary and sufficient condition for which the two bounds coincide is provided, thus exactly determining the exponent. This condition is observed to hold for a wide range of source-channel parameters. As an application, we study the advantage in terms of the excess distortion exponent of JSCC over traditional tandem (separate) coding for Gaussian systems. A formula for the tandem exponent is derived in terms of the Gaussian source and Gaussian channel exponents, and numerical results show that JSCC often substantially outperforms tandem coding. The problem of transmitting memoryless Laplacian sources over the Gaussian channel under the magnitude-error distortion is also carried out. Finally, we establish a lower bound for E-J for a certain class of continuous source-channel pairs when the distortion measure is a metric.

关键词： Continuous memoryless sources and channels error exponent excess distortion exponent Fenchel duality joint source-channel coding memoryless Gaussian and Laplacian sources memoryless Gaussian channels probability of excess distortion tandem separate source and channel coding squared/magnitude-error distortion Fenchel transform

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joint source-channel coding With Feedback

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IEEE TRANSACTIONS ON INFORMATION THEORY 2017年第6期63卷 3502-3515页

作者： Kostina, Victoria Polyanskiy, Yury Verdu, Sergio CALTECH Pasadena CA 91125 USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA Princeton Univ Princeton NJ 08544 USA

This paper quantifies the fundamental limits of variable-length transmission of a general (possibly analog) source over a memoryless channel with noiseless feedback, under a distortion constraint. We consider excess distortion, average distortion, and guaranteed distortion (d-semifaithful codes). In contrast to the asymptotic fundamental limit, a general conclusion is that allowing variable-length codes and feedback leads to a sizable improvement in the fundamental delay-distortion tradeoff. In addition, we investigate the minimum energy required to reproduce k source samples with a given fidelity after transmission over a memoryless Gaussian channel, and we show that the required minimum energy is reduced with feedback and an average (rather than maximal) power constraint.

关键词： Variable-length coding joint source-channel coding lossy compression single-shot method finite-blocklength regime rate-distortion theory feedback memoryless channels energy-distortion tradeoff

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