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D²-JSCC: Digital Deep Joint Source-channel coding for Semantic Communications

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2025年第4期43卷 1246-1261页

作者： Huang, Jianhao Yuan, Kai Huang, Chuan Huang, Kaibin Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Peoples R China Chinese Univ Hong Kong Future Network Intelligence Inst Shenzhen 518172 Peoples R China

Semantic communications (SemCom) have emerged as a new paradigm for supporting sixth-generation applications, where semantic features of data are transmitted using artificial intelligence algorithms to attain high communication efficiencies. Most existing SemCom techniques utilize deep neural networks (DNNs) to implement analog source-channel mappings, which are incompatible with existing digital communication architectures. To address this issue, this paper proposes a novel framework of digital deep joint source-channel coding (D-2-JSCC) targeting image transmission in SemCom. The framework features digital source and channel codings that are jointly optimized to reduce the end-to-end (E2E) distortion. First, deep source coding with an adaptive prior model is designed to encode semantic features according to their distributions. Second, channel coding is employed to protect encoded features against channel distortion. To facilitate their joint design, the E2E distortion is characterized as a function of the source and channel rates via the analysis of the Bayesian model and Lipschitz assumption on the DNNs. Then to minimize the E2E distortion, a two-step algorithm is proposed to control the source-channel rates for a given channel signal-to-noise ratio. Simulation results reveal that the proposed framework outperforms classic deep JSCC and mitigates the cliff and leveling-off effects, which commonly exist for separation-based approaches.

关键词： Distortion channel coding Source coding Semantics Decoding Signal to noise ratio Symbols Optimization Adaptation models Image coding Semantic communications digital deep joint source-channel coding deep learning deep source coding joint source-channel rate control

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Joint Source-channel coding for Multi-channel Vibrotactile Sensors

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IEEE ACCESS 2025年 13卷 7736-7745页

作者： Ogura, Tom Kitamura, Shogo Fujihashi, Takuya Saruwatari, Shunsuke Watanabe, Takashi Osaka Univ Grad Sch Informat Sci & Technol Osaka 5650871 Japan

Wireless transmission of vibrotactile information holds great promise for immersive Extended Reality (XR) applications, particularly in enabling fine-grained remote manipulation through multi-channel vibrotactile signals. Existing vibrotactile coding solutions rely on conventional source and channel coding techniques to transmit high-quality vibrotactile data over band-limited and error-prone wireless channels. However, these methods often suffer from severe quality degradation due to bit errors and the unrecoverable source coding distortion. This study presents a novel vibrotactile communication scheme based on joint source-channel coding (JSCC) principles. The key advancement of this approach is the replacement of traditional source and channel coding with a joint source-channel vibrotactile coder and transceiver. The proposed coder compresses vibrotactile data using a two-dimensional discrete cosine transform (2D-DCT) without binarization, and the transmitter maps the DCT coefficients directly to transmission symbols. Optimized power allocation is used to avoid the catastrophic quality degradation and to maintain high vibrotactile quality under various wireless channel conditions. Experimental results using up to 30 vibrotactile sensors and 13 gestures show that the proposed scheme consistently outperforms state-of-the-art vibrotactile coding schemes, delivering high-quality vibrotactile signals over a range of wireless channels, from low to high channel signal-to-noise ratios (SNRs). In addition, the proposed scheme achieved approximately 80 % traffic reduction compared to the existing schemes under the same vibrotactile signal quality.

关键词： Accelerometers Wireless sensor networks Wireless communication Encoding Transmitters channel coding Transforms Receivers Quantization (signal) Discrete cosine transforms Multi-channel vibrotactile signal joint source-channel coding (JSCC)

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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 via Attention for Wireless Image Transmission 14th

Deep Joint Source Channel Coding via Attention for Wireles...

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14th EAI International Conference on Wireless and Satellite Systems, WiSATS 2024

作者： Chang, Haoze Ma, Lin Wang, Xuedong School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9783031861956

In digital communication, efficiently transmitting image and video data through constrained channels remains challenging nowadays. Traditional methods using separate source and channel coding often fail in dynamic environments. In this paper, we introduce a novel deep learning based (DL) attention joint source channel coding (AttenJSCC) approach, which enhances robustness and efficiency in wireless image transmissions. By integrating source and channel coding into a unified framework and incorporating our Enhanced Attention Feature (EAF) modules and the ECA attention mechanism, our method outperforms some of the existing JSCC techniques, especially in low SNR conditions. Our framework not only overcomes the limitations of current technologies but also reduces the storage and computational needs on edge devices, facilitating more efficient real time communication. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： channel coding

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channel coding for Gaussian channels with Mean and Variance Constraints

arXiv

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arXiv 2025年

作者： Mahmood, Adeel Wagner, Aaron B. School of Electrical and Computer Engineering Cornell University United States

We consider channel coding for Gaussian channels with the recently introduced mean and variance cost constraints. Through matching converse and achievability bounds, we characterize the optimal first- and second-order performance. The main technical contribution of this paper is an achievability scheme which uses random codewords drawn from a mixture of three uniform distributions on (n − 1)-spheres of radii R1, R2 and R3, where Ri= O(√n) and |Ri − Rj | = O(1). To analyze such a mixture distribution, we prove a lemma giving a uniform O(log n) bound, which holds with high probability, on the log ratio of the output distributions Qcci and Qccj , where Qcci is induced by a random channel input uniformly distributed on an (n − 1)-sphere of radius Ri. To facilitate the application of the usual central limit theorem, we also give a uniform O(log n) bound, which holds with high probability, on the log ratio of the output distributions Qcci and Q∗i , where Q∗i is induced by a random channel input with i.i.d. components. © 2025, CC BY.

关键词： channel coding

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Deep Joint Source channel coding for Privacy-Aware End-to-End Image Transmission

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 568-584页

作者： Mehdi Letafati Seyyed Amirhossein Ameli Kalkhoran Ecenaz Erdemir Babak Hossein Khalaj Hamid Behroozi Deniz Gündüz Department of Electrical Engineering Sharif University of Technology Tehran Iran Computer Engineering Department Sharif University of Technology Tehran Iran Department of Electrical and Electronic Engineering Imperial College London London U.K. Sharif Center for Information Systems and Data Science Sharif University of Technology Tehran Iran School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran

Deep neural network (DNN)-based joint source and channel coding is proposed for privacy-aware end-to-end image transmission against multiple eavesdroppers. Both scenarios of colluding and non-colluding eavesdroppers are considered. Unlike prior works that assume perfectly known and independent identically distributed (i.i.d.) source and channel statistics, the proposed scheme operates under unknown and non-i.i.d. conditions, making it more applicable to real-world scenarios. The goal is to transmit images with minimum distortion, while simultaneously preventing eavesdroppers from inferring certain private attributes of images. Simultaneously generalizing the ideas of privacy funnel and wiretap coding, a multi-objective optimization framework is expressed that characterizes the trade-off between image reconstruction quality and information leakage to eavesdroppers, taking into account the structural similarity index (SSIM) for improving the perceptual quality of image reconstruction. Extensive experiments on the CIFAR-10 and CelebA, along with ablation studies, demonstrate significant performance improvements in terms of SSIM, adversarial accuracy, and the mutual information leakage compared to benchmarks. Experiments show that the proposed scheme restrains the adversarially-trained eavesdroppers from intercepting privatized data for both cases of eavesdropping a common secret, as well as the case in which eavesdroppers are interested in different secrets. Furthermore, useful insights on the privacy-utility trade-off are also provided.

关键词： Communication system security Security Image communication Wireless sensor networks Image reconstruction Eavesdropping channel coding Autoencoders Wireless networks Training

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Dual Diagonal LDPC: A Resource-Efficient channel coding Scheme for CubeSat Applications

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Zeedan, Amr Khattab, Tamer Hamila, Ridha Zorba, Nizar Electrical Engineering Doha Qatar

The establishment of high-speed connectivity through CubeSats requires innovative solutions to overcome the challenges associated with the development of low-power, high-speed transceivers within the size and mass limitations inherent to CubeSats. Among the most computationally demanding tasks of any transceiver are demodulation and channel decoding, typically requiring a high utilization of hardware resources. This paper proposes a channel coding scheme based on Low Density Parity Check (LDPC) coding, offering a markedly efficient allocation of resources to address these computational demands. In contrast to traditional LDPC encoding approaches, we develop a simplified algorithm based on an Irregular Repeat-Accumulate (IRA) dual diagonal parity check matrix construction, resulting in an extremely resource-efficient code. The enhanced algorithm efficiently conserves hardware resources through the shared utilization of the parity check bit generator. Moreover, a corresponding low-complexity decoder based on efficiently combines both the Min-Sum (MS) algorithm and the Bit-Flipping (BF) algorithm, employing the Log-Likelihood Ratio (LLR) is proposed. We obtain the mathematical closed-form expression for the Bit Error Rate (BER) of the proposed scheme and verify it against simulations, showing a very close match. The proposed scheme achieves high code rate, 3/4, and data rate, 30 Mbps, with a smaller number of decoding iterations, 10, while achieving better BER performance compared to various existing algorithms. Compared to the channel coding schemes currently being employed by CubeSats, our scheme demonstrates the highest information rate, the lowest number of decoding iterations and the highest efficiency. © 2024 IEEE.

关键词： channel coding

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A Novel Lightweight Joint Source-channel coding Design in Semantic Communications

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IEEE Internet of Things Journal 2025年

作者： Yu, Xianhua Li, Dong Zhang, Ning Shen, Xuemin Macau University of Science and Technology School of Computer Science and Engineering 999078 China University of Windsor Department of Electrical and Computer Engineering Canada University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Semantic communication has emerged as a promising solution to meet the growing demand for efficient data transmission in the information age. Unlike traditional communication methods that focus on transmitting raw data, semantic communication prioritizes preserving the meaning of transmitted information, which significantly reduces the data volume. However, implementing semantic communication systems in resource-constrained environments, such as Internet of Things (IoT) devices, remains challenging due to limited computational resources. In this letter, we propose a novel lightweight deep learning (DL) model, termed the lightweight image compression and reconstruction network (LICRnet). LICRnet leverages depthwise separable convolution (DSC) and a local and non-local mixture (LNLM) block to significantly reduce computational costs. Additionally, the LNLM incorporates a variable window size-based multi-scale attention mechanism (VW-MSA), enabling it to effectively learn from both local detailed features and global high-level meaningful features. Extensive simulations demonstrate that LICRnet significantly reduces computational complexity while maintaining satisfactory image compression and reconstruction performance, making it highly suitable for deployment in resource-constrained environments. © 2014 IEEE.

关键词： channel coding

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Optimization and Deployment of Joint Source-channel coding for Enhance Future 6G Network Performance

Optimization and Deployment of Joint Source-Channel Coding f...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Kalyan Acharjya Arunkumar Devalapura Thimmappa Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar District Karnataka India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Research on semantic communication has advanced because of deep learning in the construction for collaborative (joint) resource-channel coding (JSCC) project. Deep learning technology (DL)-based JSCC systems currently in practice are developed based upon a preset signal-to-noise ratio (SNR); though, such design is not intended in situations when SNR is changing. Thus, new semantic adaptive system with the semantic adaption modal selectioning (SAMS) modules is developed for semantic communication (SC). It is named collaborative (joint) resource-channel coding with adaptive models (AMJSCC). Both the real-time channel circumstances as well as the model's computing capabilities are taken into account by collaborative (joint) resource-channel encoding (JSCE) & collaborative (joint) resource-channel decoding (JSCD) models. In order to further increase the precision of information recovery, residual networks with various layers are also being studied. Simulation findings show that the model is flexible with the SNR as well as communication services, and it can achieve increased recovery similarity. Meanwhile, it uses less store space and communication resources than the most advanced deep JSCC techniques.

关键词： Signal to noise ratio Computational modeling Adaptation models channel coding Semantic communication Encoding Image reconstruction Decoding Quantization (signal) Mathematical models

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Importance-Aware Source-channel coding for Multi-Modal Task-Oriented Semantic Communication

arXiv

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arXiv 2025年

作者： Ma, Yi Xu, Chunmei Liu, Zhenyu Zhang, Siqi Tafazolli, Rahim 6GIC Institute for Communication Systems University of Surrey GuildfordGU2 7XH United Kingdom

This paper explores the concept of information importance in multi-modal task-oriented semantic communication systems, emphasizing the need for high accuracy and efficiency to fulfill task-specific objectives. At the transmitter, generative AI (GenAI) is employed to partition visual data objects into semantic segments, each representing distinct, task-relevant information. These segments are subsequently encoded into tokens, enabling precise and adaptive transmission control. Building on this framework, we present importance-aware source and channel coding strategies that dynamically adjust to varying levels of significance at the segment, token, and bit levels. The proposed strategies prioritize high fidelity for essential information while permitting controlled distortion for less critical elements, optimizing overall resource utilization. Furthermore, we address the source-channel coding challenge in semantic multiuser systems, particularly in multicast scenarios, where segment importance varies among receivers. To tackle these challenges, we propose solutions such as rate-splitting coded progressive transmission, ensuring flexibility and robustness in task-specific semantic communication. Copyright © 2025, The Authors. All rights reserved.

关键词： channel coding

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