Rateless Joint Source-Channel Coding, and a Blueprint for 6G Semantic Communications System Design
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 23 of its 57 references that are in the map. First posted on arXiv 10 Feb 2025.
Builds on
23 of 57 references.
References · 23
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,223
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,360
- 2017Residual Attention Network for Image Classificationcited by 386
- 2020Deep Learning Enabled Semantic Communication Systemscited by 289
- 2022Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communicationscited by 172
- 2021Semantic Communications: Principles and Challengescited by 90
- 2016Variational inference for Monte Carlo objectivescited by 84
- 2022Deep Hierarchical Semantic Segmentationcited by 84
- 2020An Overview of Neural Network Compressioncited by 59
- 2020Wireless Image Retrieval at the Edgecited by 58
- 2020DeepRx: Fully Convolutional Deep Learning Receivercited by 52
- 2020From Semantic Communication to Semantic-aware Networking: Model, Architecture, and Open Problemscited by 52
11 more
- 2021What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligencecited by 50
- 2022Deep Joint Source-Channel Coding for Semantic Communicationscited by 41
- 2024Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designscited by 32
- 2020Stochastic Bottleneck: Rateless Auto-Encoder for Flexible Dimensionality Reductioncited by 13
- 2024Generative Visual Compression: A Reviewcited by 13
- 2024Next Generation Advanced Transceiver Technologies for 6G and Beyondcited by 8
- 2024Evolving Semantic Communication with Generative Modelcited by 8
- 2023Secure Deep-JSCC Against Multiple Eavesdropperscited by 7
- 2023Semantic Communications with Variable-Length Coding for Extended Realitycited by 5
- 2024Multi-level Reliability Interface for Semantic Communications over Wireless Networkscited by 4
- 2024Semantic Communication for the Internet of Sounds: Architecture, Design Principles, and Challengescited by 1
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