# Rateless Joint Source-Channel Coding, and a Blueprint for 6G Semantic Communications System Design

arXiv: https://arxiv.org/abs/2502.06095 · HTML: https://mlsanity.com/paper/2502.06095/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 57 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Builds on (most cited first, 23 of 23)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Residual Attention Network for Image Classification](https://mlsanity.com/paper/1704.06904/) (2017)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [Semantic Communications: Principles and Challenges](https://mlsanity.com/paper/2201.01389/) (2021)
- [Variational inference for Monte Carlo objectives](https://mlsanity.com/paper/1602.06725/) (2016)
- [Deep Hierarchical Semantic Segmentation](https://mlsanity.com/paper/2203.14335/) (2022)
- [An Overview of Neural Network Compression](https://mlsanity.com/paper/2006.03669/) (2020)
- [Wireless Image Retrieval at the Edge](https://mlsanity.com/paper/2007.10915/) (2020)
- [DeepRx: Fully Convolutional Deep Learning Receiver](https://mlsanity.com/paper/2005.01494/) (2020)
- [From Semantic Communication to Semantic-aware Networking: Model, Architecture, and Open Problems](https://mlsanity.com/paper/2012.15405/) (2020)
- [What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence](https://mlsanity.com/paper/2110.00196/) (2021)
- [Deep Joint Source-Channel Coding for Semantic Communications](https://mlsanity.com/paper/2211.08747/) (2022)
- [Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designs](https://mlsanity.com/paper/2409.17557/) (2024)
- [Stochastic Bottleneck: Rateless Auto-Encoder for Flexible Dimensionality Reduction](https://mlsanity.com/paper/2005.02870/) (2020)
- [Generative Visual Compression: A Review](https://mlsanity.com/paper/2402.02140/) (2024)
- [Next Generation Advanced Transceiver Technologies for 6G and Beyond](https://mlsanity.com/paper/2403.16458/) (2024)
- [Evolving Semantic Communication with Generative Model](https://mlsanity.com/paper/2403.20237/) (2024)
- [Secure Deep-JSCC Against Multiple Eavesdroppers](https://mlsanity.com/paper/2308.02892/) (2023)
- [Semantic Communications with Variable-Length Coding for Extended Reality](https://mlsanity.com/paper/2302.08645/) (2023)
- [Multi-level Reliability Interface for Semantic Communications over Wireless Networks](https://mlsanity.com/paper/2407.05487/) (2024)
- [Semantic Communication for the Internet of Sounds: Architecture, Design Principles, and Challenges](https://mlsanity.com/paper/2407.12203/) (2024)

## Built on by (most cited first, 1 of 1)

- [Generative Decompression: Optimal Lossy Decoding Against Distribution Mismatch](https://mlsanity.com/paper/2602.03505/) (2026)

## 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.