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作者： Akpabio, Inimfon Texas A&M University

Most modern communication systems rely on separate source encoding and channel encoding schemes to transmit data. Despite the long-lasting success of separate schemes, joint source channel coding schemes have been proven to outperform separate schemes in applications such as video communications. The task of this research is to develop a joint source-channel coding scheme that mitigates some of the limitations of current separate coding schemes. My research will attempt to leverage recent advances in machine/deep learning techniques to develop resilient schemes that do not depend on explicit codes for compression and error correction but automatically learn end-to-end mapping schemes for source signals. The success of the developed scheme will depend on its ability to correctly approximate an input vector under inconsistent channel conditions.

关键词： joint source-channel coding Deep-learning Bidirectional Long short-term memory Convlutional neural network Autoencoder Recurrent neural network Bit error rate Block error rate Thesis

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A Type-Aware coding Approach to joint source-channel coding

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IEEE COMMUNICATIONS LETTERS 2021年第11期25卷 3454-3457页

作者： Yao, Xinyuanmeng Ma, Xiao Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Sun Yat Sen Univ Guangdong Key Lab Informat Secur Technol Guangzhou 510006 Peoples R China

In this letter, we propose a type-aware coding approach to joint source-channel coding (JSCC) and present a concrete construction with polarization-adjusted convolutional (PAC) codes. A data sequence is first encoded by enumerative coding and then encoded by a data-dependent PAC code, resulting in a transmitted codeword. At the receiver, a type-search decoding algorithm is employed, and the implementation complexity can be reduced by searching only those typical types. Simulation results show that the presented JSCC scheme can outperform the conventional tandem coding scheme with a fixed-to-fixed-length source code and achieve a performance within the gap between the upper and lower bound on the minimum FER of finite-length JSCCs.

关键词： Codes Encoding Decoding Picture archiving and communication systems Indexes Convolutional codes Upper bound Enumerative coding fixed-to-variable-length joint source-channel coding PAC codes type-aware coding

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Optimal Rate for joint source-channel coding of Correlated sources Over Orthogonal channels

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IEEE COMMUNICATIONS LETTERS 2015年第1期19卷 22-25页

作者： Pujaico, Fernando Portugheis, Jaime Univ Estadual Campinas Dept Commun BR-13083852 Campinas SP Brazil Univ Estadual Campinas Sch Technol BR-13484332 Limeira SP Brazil

joint source-channel coding of multiple correlated sources transmitted over orthogonal channels is considered. Two optimization problems related to coding schemes are formulated: maximal common rate, and maximal sum rate. These two problems model different power saving situations in energy-constrained wireless sensor networks. Solutions of these problems for an arbitrary number of sources are described, and some specific examples are given.

关键词： Distributed source coding joint source-channel coding optimal rate

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A New joint source-channel coding Scheme Based on Nested Lattice Codes

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IEEE COMMUNICATIONS LETTERS 2012年第5期16卷 730-733页

作者： Yang, Zheng Zhao, Shancheng Ma, Xiao Bai, Baoming Sun Yat Sen Univ Dept Elect & Commun Engn Guangzhou 510275 Guangdong Peoples R China Xidian Univ State Key Lab ISN Xian 710071 Peoples R China

In this paper, we propose a new joint source-channel coding (JSCC) method for transmission of a Gaussian source through an additive white Gaussian noise (AWGN) channel whose bandwidth is higher than that of the source. Firstly, a sequence of random variables from a Gaussian source is discretized with a lattice-based quantizer, resulting in a sequence of lattice points. Secondly, by driving an encoder of a systematic group code, these lattice points are utilized to compute a sequence of parity lattice points. Thirdly, both the systematic and the parity lattice points are scaled and transmitted over an AWGN channel. Simulation results show that, with moderate code length, our scheme performs well when compared with the Shannon limits.

关键词： Group codes joint source-channel coding nested lattice codes Kite codes

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Robust joint source-channel coding for image transmission over wireless channels

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2000年第6期10卷 962-966页

作者： Cai, JF Chen, CW Univ Missouri Dept Elect Engn Columbia MO 65211 USA

In this letter, we present a fixed-length robust joint source-channel coding (RJSCC) scheme for transmitting images over wireless channels. The system integrates a joint source-channel coding (JSCC) scheme with all-pass filtering source shaping to enable robust image transmission. In particular, we are able to incorporate both transition probability and bit error rate of a bursty channel model into an end-to-end rate-distortion (R-D) function to achieve an optimum tradeoff between source coding accuracy and channel error protection under a fixed transmission rate. Experimental results show that the proposed scheme can achieve not only high peak signal-to-noise ratio performance, but also excellent perceptual quality, especially when the channel mismatch occurs.

关键词： bit allocation image communication joint source-channel coding source reshaping wireless communications

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An Efficient joint source-channel coding Scheme for Wireless Hierarchical Federated Learning and its Information-Theoretic Analysis

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第4期70卷 7398-7411页

作者： Zhang, Haonan Li, Ke Zhao, Kangli Xu, Peng Dai, Bin Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Peoples R China Southwest Jiaotong Univ Prov Key Lab Informat Coding & Transmiss Chengdu 611756 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Mobile Commun Technol Chongqing 400065 Peoples R China Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Mobile Commun Technol Chongqing 400065 Peoples R China

Wireless hierarchical federated learning (WHFL) is an implementation of wireless federated learning (WFL) on a cloud-edge-client hierarchical architecture that accelerates model training and achieves more favorable trade-offs between communication and computation. However, the unreliable wireless channel causes aggregation distortion and transmission latency, which significantly influence the learning performance of WHFL. To solve these problems, this paper proposes a feedback oriented joint source-channel coding (FO-JSCC) scheme for the multiple-input multiple-output (MIMO) WHFL system, which exploits channel output feedback, and provides significant improvements in gradient distortion under fixed coding blocklength or in transmission latency under target gradient distortion. Besides this, we analyze the impact of gradient distortion on the convergence rate and learning efficiency of WHFL. Simulation results show that our proposed scheme reduces model gradient distortion and enhances learning performance, and the transmission latency of our proposed scheme is about 2-4 times lower than that of traditional coding schemes.

关键词： Servers Wireless communication Encoding Distortion Wireless sensor networks Federated learning Communication system security channel feedback hierarchical federated learning joint source-channel coding wireless edge computing

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Deep joint source-channel coding for DNA Image Storage: A Novel Approach With Enhanced Error Resilience and Biological Constraint Optimization

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IEEE TRANSACTIONS ON MOLECULAR BIOLOGICAL AND MULTI-SCALE COMMUNICATIONS 2023年第4期9卷 461-471页

作者： Wu, Wenfeng Xiang, Luping Liu, Qiang Yang, Kun Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Yangtze Delta Reg Inst Quzhou Quzhou 324000 Zhejiang Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ England

In the current era, DeoxyriboNucleic Acid (DNA) based data storage emerges as an intriguing approach, garnering substantial academic interest and investigation. This paper introduces a novel deep joint source-channel coding (DJSCC) scheme for DNA image storage, designated as DJSCC-DNA. This paradigm distinguishes itself from conventional DNA storage techniques through three key modifications: 1) it employs advanced deep learning methodologies, employing convolutional neural networks for DNA encoding and decoding processes;2) it seamlessly integrates DNA polymerase chain reaction (PCR) amplification into the network architecture, thereby augmenting data recovery precision;and 3) it restructures the loss function by targeting biological constraints for optimization. The performance of the proposed model is demonstrated via numerical results from specific channel testing, suggesting that it surpasses conventional deep learning methodologies in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Additionally, the model effectively ensures positive constraints on both homopolymer run-length and GC content.

关键词： DNA storage deep learning joint source-channel coding biological constraints

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DeepJSCC-Q: Constellation Constrained Deep joint source-channel coding

IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY

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IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY 2022年第4期3卷 720-731页

作者： Tung, Tze-Yang Kurka, David Burth Jankowski, Mikolaj Gunduz, Deniz Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England

Recent works have shown that modern machine learning techniques can provide an alternative approach to the long-standing joint source-channel coding (JSCC) problem. Very promising initial results, superior to popular digital schemes that utilize separate source and channel codes, have been demonstrated for wireless image and video transmission using deep neural networks (DNNs). However, end-to-end training of such schemes requires a differentiable channel input representation;hence, prior works have assumed that any complex value can be transmitted over the channel. This can prevent the application of these codes in scenarios where the hardware or protocol can only admit certain sets of channel inputs, prescribed by a digital constellation. Herein, we propose DeepJSCC-Q, an end-to-end optimized JSCC solution for wireless image transmission using a finite channel input alphabet. We show that DeepJSCC-Q can achieve similar performance to prior works that allow any complex valued channel input, especially when high modulation orders are available, and that the performance asymptotically approaches that of unconstrained channel input as the modulation order increases. Importantly, DeepJSCC-Q preserves the graceful degradation of image quality in unpredictable channel conditions, a desirable property for deployment in mobile systems with rapidly changing channel conditions.

关键词： joint source-channel coding wireless image transmission image compression deep neural networks deep learning

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Minimum Energy Analysis for Robust Gaussian joint source-channel coding With a Distortion-Noise Profile

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IEEE TRANSACTIONS ON INFORMATION THEORY 2022年第12期68卷 7702-7713页

作者： Baniasadi, Mohammadamin Koken, Erman Tuncel, Ertem Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA Univ Edinburgh Sch Engn Inst Digital Commun Edinburgh EH8 9YL Midlothian Scotland Qualcomm Inc San Diego CA 92121 USA

Minimum energy required to achieve a distortion-noise profile, i.e., a function indicating the maximum allowed distortion value for each channel noise level, is studied for transmission of Gaussian sources over Gaussian channels. A general coding scheme is proposed to upper bound the minimum energy needed for any distortion-noise profile. Conversely, utilizing a family of lower bounds originally derived for broadcast channels with power constraints, the minimum required energy is lower bounded for any profile. As examples, linear, exponential, square-law, and staircase profiles are studied. It is shown that for the linear profile, as well as for any concave profile, uncoded transmission is optimal. As a negative result, it is shown that exponential profiles are not achievable with finite energy. For square-law and staircase profiles, upper bounds and lower bounds are derived and the gaps between them are studied.

关键词： Distortion Noise level Bandwidth Receivers Upper bound Signal to noise ratio Encoding Distortion-noise profile energy-distortion tradeoff energy-limited transmission fidelity-quality profile joint source-channel coding

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DeepJSCC-f : Deep joint source-channel coding of Images With Feedback

IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY

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IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY 2020年第1期1卷 178-193页

作者： Kurka, David Burth Gunduz, Deniz Imperial Coll London Elect & Elect Engn Dept London SW7 2AZ England

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.

关键词： Feedback channel joint source-channel coding deep neural networks image communications

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