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Conference on Advanced Signal Processing Algorithms, Architectures, and Implementations XI

作者： Su, YJ Wang, J Chen, JS Lu, JH Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

ISBN: (纸本)0819441880

This paper proposes a new methodology of joint optimization among source coding, channel coding and error concealment for wireless video transmission. Specifically, a generalized distortion-rate function (GDRF), which reflects the relation between the source coding rates, channel coding rates and the end-to-end distortion consisting of quantization distortion, channel loss distortion compensated by error concealment, is established. Moreover. a classification algorithm with modified equal mean-normalized standard deviation is proposed to employ the GDRF for optimal source-channel bit-allocation. Simulation results manifest that the proposed methodology outperforms conventional joint source-channel coding (JSCC) schemes, with computational complexity being quite acceptable for real time video applications.

关键词： joint source-channel coding error concealment generalized distortion-rate function

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GAI-Enhanced Robust Semantic Communication With Asymmetric Architecture

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2025年第2期11卷 700-711页

作者： Ren, Pengfei Wang, Jingjing Hou, Xiangwang Chen, Jianrui Jiang, Chunxiao Beihang Univ Sch Cyber Sci & Technol Beijing 100191 Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou 310051 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China

Semantic communication (SC), regarded as a next-generation communication architecture that breaks through the Shannon paradigm, is considered a key technology for realizing future sixth-generation wireless networks and cognitive communications. Instead of focusing on the bit error rate, SC is dedicated to extracting abstract semantic information from original data to enhance communication efficiency for specific tasks. However, current SC systems mostly rely on symmetric architectures based on convolutional neural networks, which not only severely limits the capacity of the network but also leads to a high degree of coupling between the encoder and decoder. Additionally, it also lacks robustness in noise reference. The emergence of generative artificial intelligence (GAI) breaks this bottleneck. In this paper, we propose an asymmetric end-to-end SC architecture based on GAI, named masked joint source-channel coding (M-JSCC). In our model, the encoder serves as a universal semantic extractor, while the decoder is tailored to specific tasks. During the model training, we introduce a masking mechanism that improves the performance of M-JSCC to extract semantic information and enhances the robustness under various channel conditions. Moreover, it also endows M-JSCC with remarkable data generation abilities. Benefiting from the asymmetric architecture, the decoder no longer depends on the encoder, which allows it to be switched according to the specific requirements to better adapt to different task-oriented scenarios. Finally, comprehensive experiments demonstrate the excellent semantic understanding and communication robustness of M-JSCC.

关键词： Semantics Decoding Data mining Signal to noise ratio Image reconstruction channel coding Robustness Adaptation models Training Data models Semantic communication generative artificial intelligence joint source-channel coding image reconstruction

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Density Evolution for joint source-channel Asymmetric Slepian-Wolf coding with Parity Based Approach

Density Evolution for Joint Source-channel Asymmetric Slepia...

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18th Asia-Pacific Conference on Communications (APCC) - Green and Smart Communications for IT Innovation

作者： Cen, Feng Zhu, Fanglai Tongji Univ Dept Control Sci & Engn 1239 Siping Rd Shanghai 200092 Peoples R China

ISBN: (纸本)9781467347280

In this paper, iterative codeword-averaged density evolution (DE) formulas for low-density parity-check (LDPC) codes are developed for joint source-channel asymmetric Slepian-Wolf (ASW) coding of two correlated binary nonuniform memoryless sources with parity based approach. In order to be compatible with the parity based approach, a new ensemble definition of irregular codes being able to distinguish the degree distributions of the edges incident to information variable nodes and the edges incident to parity-checking variable nodes is introduced. Extensive simulations demonstrate the effectiveness of the proposed DE for joint source-channel ASW coding with parity based approach.

关键词： Slepian-Wolf coding joint source-channel coding density evolution LDPC codes nonuniform sources

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joint source-channel Secrecy Using Hybrid coding

Joint Source-Channel Secrecy Using Hybrid Coding

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IEEE International Symposium on Information Theory (ISIT)

作者： Song, Eva C. Cuff, Paul Poor, H. Vincent Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

ISBN: (纸本)9781467377041

The secrecy performance of a source-channel model is studied in the context of lossy source compression over a noisy broadcast channel. The source is causally revealed to the eavesdropper during decoding. The fidelity of the transmission to the legitimate receiver and the secrecy performance at the eavesdropper are both measured by a distortion metric. Two achievability schemes using the technique of hybrid coding are analyzed and compared with an operationally separate source channel coding scheme. A numerical example is provided and the comparison results show that the hybrid coding schemes outperform the operationally separate scheme.

关键词： hybrid coding likelihood encoder joint source-channel coding secrecy wiretap channel

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Rate-Adaptive Generative Semantic Communication Using Conditional Diffusion Models

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第2期14卷 539-543页

作者： Yang, Pujing Zhang, Guangyi Cai, Yunlong Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Peng Cheng Lab Shenzhen 518071 Peoples R China

Recent advances in deep learning-based joint source-channel coding (DJSCC) have shown promise for end-to-end semantic image transmission. However, most existing schemes primarily focus on optimizing pixel-wise metrics, which often fail to align with human perception, leading to lower perceptual quality. In this letter, we propose a novel generative DJSCC approach using conditional diffusion models to enhance the perceptual quality of transmitted images. Specifically, by utilizing entropy models, we effectively manage transmission bandwidth based on the estimated entropy of transmitted symbols. These symbols are then used at the receiver as conditional information to guide a conditional diffusion decoder in image reconstruction. Our model is built upon the emerging advanced mamba-like linear attention (MLLA) skeleton, which excels in image processing tasks while also offering fast inference speed. Besides, we introduce a multi-stage training strategy to ensure the stability and improve the overall performance of the model. Simulation results demonstrate that our proposed method significantly outperforms existing approaches in terms of perceptual quality.

关键词： Semantic communications conditional diffusion models joint source-channel coding image transmission

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joint vs. Separate source-channel coding in CSI Feedback for Massive MIMO 59

Joint vs. Separate Source-Channel Coding in CSI Feedback for...

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59th Annual IEEE International Conference on Communications (IEEE ICC)

作者： Song, Yi Yang, Tianyu Khalilsarai, Mahdi Barzegar Caire, Giuseppe Tech Univ Berlin Fac Elect Engn & Comp Sci Berlin Germany Lenovo Res Wireless Res & Stand Stuttgart Germany

ISBN: (纸本)9781728190549

In this work, we study and compare two types of CSI feedback schemes in multi-user massive MIMO systems, respectively based on joint and separate source-channel coding (JSCC and SSCC). Using the classical remote distortion-rate (DR) theory, we first provide a theoretical lower bound on the channel estimation mean-square-error (MSE) of any feedback scheme. The DR bound is achieved by using vector quantization applied to long sequences of channel state estimates and requires capacity-achieving channel coding in the uplink, resulting in a large delay in the CSI feedback loop that makes the scheme impractical. Thus we propose a practical JSCC-based feedback scheme that sends the CSI with minimal delay. Unlike previous works that simply apply linear mapping and equal power allocation to generate the feedback signal, our method applies the dimension projection in the eigenspace and optimizes power allocation by fully exploiting the channel second-order statistics. The extensive numerical results show that our proposed JSCC-based scheme not only outperforms the previous JSCC scheme with linear processing but also produces lower channel estimate MSE compared to a standard SSCC-based scheme at practical SNR.

关键词： joint source-channel coding CSI feedback Distortion-Rate Theory

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Topology Data Analysis-Based Error Detection for Semantic Image Transmission with Incremental Knowledge-Based HARQ

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China Communications 2025年第1期22卷 235-255页

作者： Ni Fei Li Rongpeng Zhao Zhifeng Zhang Honggang College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310027China Zhejiang Lab Hangzhou 311121China Faculty of Data Science City University of MacaoMacaoChina

Semantic communication(SemCom)aims to achieve high-fidelity information delivery under low communication consumption by only guaranteeing semantic ***,semantic communication still suffers from unexpected channel volatility and thus developing a re-transmission mechanism(e.g.,hybrid automatic repeat request[HARQ])becomes *** that regard,instead of discarding previously transmitted information,the incremental knowledge-based HARQ(IK-HARQ)is deemed as a more effective mechanism that could sufficiently utilize the information ***,considering the possible existence of semantic ambiguity in image transmission,a simple bit-level cyclic redundancy check(CRC)might compromise the performance of ***,there emerges a strong incentive to revolutionize the CRC mechanism,thus more effectively reaping the benefits of both SemCom and *** this paper,built on top of swin transformer-based joint source-channel coding(JSCC)and IK-HARQ,we propose a semantic image transmission framework *** particular,different from the conventional CRC,we introduce a topological data analysis(TDA)-based error detection method,which capably digs out the inner topological and geometric information of images,to capture semantic information and determine the necessity for *** numerical results validate the effectiveness and efficiency of the proposed SC-TDA-HARQ framework,especially under the limited bandwidth condition,and manifest the superiority of TDA-based error detection method in image transmission.

关键词： error detection incremental knowledgebased HARQ joint source-channel coding semantic communication swin transformer topological data analysis

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Semantic Communication Over channels With Insertions, Deletions, and Substitutions

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IEEE COMMUNICATIONS LETTERS 2025年第3期29卷 586-590页

作者： Ozates, Tuna Koc, Aykut Bilkent Univ Dept Elect & Elect Engn TR-06800 Ankara Turkiye Bilkent Univ UMRAM TR-06800 Ankara Turkiye

We present deep joint source-outer channel coding (DeepJSOC), an end-to-end deep learning-based semantic communication architecture designed for channels with insertions, deletions, and substitutions (IDS). We propose a three-stage training algorithm that combines, for the first time, gated recurrent unit (GRU) networks for marker detection, transformer-based semantic communication for continuous latent space, and lookup-free quantization for binarized latent space optimization, specifically tailored to IDS channels. The proposed DeepJSOC is the first to integrate deep learning-based error correction networks into joint-source channel coding schemes for binary channels with synchronization errors. We demonstrate the effectiveness of DeepJSOC by experiments, achieving significant improvements over existing methods in text transmission.

关键词： Semantic communication joint source-channel coding joint source-channel coding IDS channel IDS channel deep learning deep learning

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joint source-channel RATE CONTROL FOR PIXEL-DOMAIN DISTRIBUTED VIDEO coding

JOINT SOURCE-CHANNEL RATE CONTROL FOR PIXEL-DOMAIN DISTRIBUT...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chen, Hu Steinbach, Eckehard Chen, Chang Wen Tech Univ Munich Inst Media Technol D-80333 Munich Germany SUNY Buffalo Dept Comp Sci & Engn Buffalo NY 14260 USA

ISBN: (纸本)9781457705397

We study the scenario of pixel-domain distributed video coding for noisy transmission environments and propose a method to allocate the available rate between source coding and channel coding to generate a robust video stream. Having observed in experiments the uncertainty of the source and the channel coding rate, we model them as random variables via offline training, estimate the decoding failure probability and calculate the mean end-to-end distortion. Adaptive quantization is performed for each slice to minimize its mean end-to-end distortion. With this joint source-channel rate allocation, we compare the robustness of two coding prototypes, namely distributed video coding and distributed video coding with forward error correction. According to our experimental results, under same total bit budget, the distributed video coding only scheme proves more robust than the latter one and the gain is up to 1 dB in PSNR.

关键词： Distributed video coding joint source-channel coding error-resilience

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ANALOG joint source-channel MULTIPLE DESCRIPTION coding SCHEME OVER AWGN PARALLEL channelS

ANALOG JOINT SOURCE-CHANNEL MULTIPLE DESCRIPTION CODING SCHE...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Erdozain, Aitor Crespo, Pedro M. Beferull, Baltasar Univ Navarra CEIT Donostia San Sebastian 20009 Spain Univ Valencia Inst Robot & Technol Informat & Commun Valencia 46980 Spain

ISBN: (纸本)9781457705397

We propose a low complexity analog joint source channel coding Multiple Description (MD) scheme for transmitting the symbols of a Gaussian source across a pair of independent AWGN channels. The outputs of these channels have each a separated receiver, whereas a third receiver has both outputs available. At the transmitter side, a pair of bandwidth-reduction analog mappings are used for joint source-channel coding. The presented scheme has the inherent advantage over digital MD schemes based on separation, that coding and decoding can be performed by using a single-letter (or symbol), a strategy that is very suitable for applications where latency originated by the digital compression and the error correcting coding can not be tolerated. Our scheme achieves a performance that is competitive as compared to the optimal region while having a very low complexity and delay.

关键词： Multiple Description joint source-channel coding

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