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

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

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

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

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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

Rateless Joint Source-channel coding, and a Blueprint for 6G Semantic Communications System Design

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arXiv 2025年

作者： Khosravirad, Saeed R. Nokia Bell Labs United States

This paper introduces rateless joint source-channel coding (rateless JSCC). The code is rateless in that it is designed and optimized for a continuum of coding rates such that it achieves a desired distortion for any rate in that continuum. We further introduce rate-adaptive and stable communication link operation to accommodate rateless JSCCs. The link operation resembles a "bit pipe" that is identified by its rate in bits per frame, and, by the rate of bits that are flipped in each frame. Thus, the link operation is rate-adaptive such that it punctures the rateless JSCC codeword to adapt its length (and coding rate) to the underlying channel capacity, and is stable in maintaining the bit flipping ratio across time frames. Next, a new family of autoencoder rateless JSCC codes are introduced. The code family is dubbed RLACS code (read as relax code, standing for ratelss and lossy autoencoder channel and source code). The code is tested for reconstruction loss of image signals and demonstrates powerful performance that is resilient to variation of channel quality. RLACS code is readily applicable to the case of semantic distortion suited to variety of semantic and effectiveness communications use cases. In the second part of the paper, we dive into the practical concerns around semantic communication and provide a blueprint for semantic networking system design relying on updating the existing network systems with some essential modifications. We further outline a comprehensive list of open research problems and development challenges towards a practical 6G communications system design that enables semantic networking. © 2025, CC BY.

关键词： channel coding

Downlink CSIT under Compressed Feedback: Joint vs. Separate Source-channel coding

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IEEE Transactions on Wireless Communications 2025年

作者： Song, Yi Yang, Tianyu Khalilsarai, Mahdi Barzegar Caire, Giuseppe Technical University of Berlin Faculty of Electrical Engineering and Computer Science Berlin10587 Germany Lenovo Research Department of Wireless Research Department of Standards Stuttgart Germany

The acquisition of Downlink (DL) channel state information at the transmitter (CSIT) is known to be a challenging task in multiuser massive MIMO systems when uplink/downlink channel reciprocity does not hold (e.g., in frequency division duplexing systems). From a coding viewpoint, the DL channel state acquired at the users via DL training can be seen as an information source that must be conveyed to the base station via the UL communication channels. The transmission of a source through a channel can be accomplished either by separate or joint source-channel coding (SSCC or JSCC). In this work, using classical remote distortion-rate (DR) theory, we first provide a theoretical lower bound on the channel estimation meansquare-error (MSE) of both JSCC and SSCC-based feedback schemes, which however requires encoding of large blocks of successive channel states and thus cannot be used in practice since it would incur in an extremely large feedback delay. We then focus on the relevant case of minimal (one slot) feedback delay and propose a practical JSCC-based feedback scheme that fully exploits the channel second-order statistics to optimize the dimension projection in the eigenspace. We analyze the large SNR behavior of the proposed JSCC-based scheme in terms of the quality scaling exponent (QSE). Given the second-order statistics of channel estimation of any feedback scheme, we further derive the closed-form of the lower bound to the ergodic sum-rate for DL data transmission under maximum ratio transmission and zero-forcing precoding. Via extensive numerical results, we show that our proposed JSCC-based scheme outperforms known JSCC, SSCC baseline and deep learning-based schemes and is able to approach the performance of the optimal DR scheme in the range of practical SNR. © 2002-2012 IEEE.

关键词： channel coding

A Contemporary Survey on Semantic Communications: Theory of Mind, Generative AI, and Deep Joint Source-channel coding

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arXiv 2025年

作者： Nguyen, Loc X. Raha, Avi Deb Aung, Pyae Sone Niyato, Dusit Han, Zhu Hong, Choong Seon Department of Computer Science and Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

Semantic Communication is becoming the next pillar in wireless communication technology due to its various capabilities. However, it still encounters various challenging obstacles that need to be solved before real-world deployment. The major challenge is the lack of standardization across different directions, leading to variations in interpretations and objectives. In the survey, we provide detailed explanations of three leading directions in semantic communications, namely Theory of Mind, Generative AI, Deep Joint Source-channel coding. These directions have been widely studied, developed, and verified by institutes worldwide, and their effectiveness has increased along with the advancement in technology. We first introduce the concepts and background of these directions. Firstly, we introduce the Theory of Mind, where the communication agents interact with each other, gaining understanding from observations and slowly forming a common language. Secondly, we present generative AI models, which can create new content and offer more freedom to interpret the data beyond the limitation of semantic meaning compression of raw data before transmitting it. The received signal is then decoded by another generative AI model to execute the oriented task. Thirdly, we review deep learning models to jointly optimize the source and channel coding modules. Then, we present a comprehensive survey of existing works in each direction, thereby offering readers an overview of past achievements and potential avenues for further contribution. Moreover, for each direction, we identify and discuss the existing challenges that must be addressed before these approaches can be effectively deployed in real-world scenarios. These challenges encompass a range of technical, computational, and practical issues. For instance, scalability and adaptability remain critical barriers in real-world deployment, particularly in dynamic environments with diverse user demands. Additionally, the possibility

关键词： channel coding

Joint Source and channel coding for Multi-Modal Satellite-to-Ground Semantic Communications

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Joint Source and Channel Coding for Multi-Modal Satellite-to...

IEEE Conference on Wireless Communications and Networking

作者： Yanbo Yin Shu Liu Dingzhu Wen Youlong Wu Yuanming Shi School of Information Science and Technology Shanghaitech University

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

This paper presents a novel Joint Source and channel coding (JSCC) method for semantic communication to enhance the communication efficiency for transmitting high-resolution multi-modal data from Low Earth Orbit (LEO) satellites to ground stations. On the satellite, a JSCC encoder consisting of neural networks (NNs) is utilized to map the input multi-modal data into a common signal, while an NN-based JSCC decoder at the ground station reconstructs the original input from the received signal. Throughout this process, the common semantic information among each modality is learned to optimize coding space, and a robust coding scheme specific to the satellite downlink channel is developed between the encoder and decoder. Experiments have demonstrated that the proposed method outperforms existing signal modality JSCC approaches, achieving multi-modal transmission with significantly smaller coding space and thereby reducing communication overhead.

关键词： Space vehicles Visualization Satellites Simulation Low earth orbit satellites Artificial neural networks Semantic communication Downlink Decoding channel coding

Concatenated channel coding with Ring Structure in Screen-Camera Links

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Concatenated Channel Coding with Ring Structure in Screen-Ca...

International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Tzu-Hao Chang Hsu-Feng Hsiao Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

This paper presents a novel approach to screen-camera communication, addressing the growing demand for efficient data transmission in visible light communication systems. We introduce Concatenated channel coding with Ring structure for unequal error protection (CCRCode), a robust coding scheme designed to overcome the challenges of screen-camera links. CCRCode leverages a two-tier error correction strategy: an intra-symbol level using Low Density Parity Check (LDPC) codes for data correctness, and an inter-symbol level employing a unique ring-structured layer-aligned multipriority rateless codes with additional LDPC codes for data integrity and unequal error protection. Our method outperforms existing techniques in handling heterogeneous data with varying importance levels. We develop an optimization model that dynamically adjusts coding parameters based on channel conditions, ensuring optimal performance across diverse scenarios. Extensive experiments demonstrate CCRCode's superior efficacy in terms of recovery rate and utility under various challenging conditions, including different distances, viewing angles, and screen shifts. Notably, CCRCode achieves complete data recovery in half the time required by competing methods.

关键词： Structural rings Optimization models Data integrity Parity check codes Real-time systems Error correction codes Protection channel coding Optimization Visible light communication

SNR-EQ-JSCC: Joint Source-channel coding with SNR-Based Embedding and Query

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arXiv 2025年

作者： Zhang, Hongwei Tao, Meixia Shanghai Jiao Tong University Shanghai China

Coping with the impact of dynamic channels is a critical issue in joint source-channel coding (JSCC)-based semantic communication systems. In this paper, 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. Copyright © 2025, The Authors. All rights reserved.

关键词： channel coding

Maximum channel coding Rate of Finite Block Length MIMO Faster-Than-Nyquist Signaling

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Maximum Channel Coding Rate of Finite Block Length MIMO Fast...

IEEE Conference on Wireless Communications and Networking

作者： Zichao Zhang Melda Yuksel Halim Yanikomeroglu Benjamin K. Ng Chan–Tong Lam Department of Systems and Computer Engineering Carleton University Ottawa ON Canada Department of Electrical and Electronics Engineering Middle East Technical University Ankara Turkey Faculty of Applied Sciences Macao Polytechnic University Macao SAR China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The pursuit of higher data rates and efficient spectrum utilization in modern communication technologies necessitates novel solutions. In order to provide insights into improving spectral efficiency and reducing latency, this study investigates the maximum channel coding rate (MCCR) of finite block length (FBL) multiple-input multiple-output faster-than-Nyquist (FTN) channels. By optimizing power allocation, we derive the system's MCCR expression. Simulation results are compared with the existing literature to reveal the benefits of FTN in FBL transmission.

关键词： Spectral efficiency Error probability Simulation Ultra reliable low latency communication MIMO Resource management channel coding Gain