# Multi-level Reliability Interface for Semantic Communications over Wireless Networks

arXiv: https://arxiv.org/abs/2407.05487 · HTML: https://mlsanity.com/paper/2407.05487/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 48 references that are in the map.
- First posted on arXiv 7 Jul 2024.

## Builds on (most cited first, 27 of 27)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules](https://mlsanity.com/paper/2001.01568/) (2020)
- [Importance Weighted Autoencoders](https://mlsanity.com/paper/1509.00519/) (2015)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [CompressAI: a PyTorch library and evaluation platform for end-to-end compression research](https://mlsanity.com/paper/2011.03029/) (2020)
- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [Density Modeling of Images using a Generalized Normalization Transformation](https://mlsanity.com/paper/1511.06281/) (2015)
- [Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2207.00427/) (2022)
- [Variational inference for Monte Carlo objectives](https://mlsanity.com/paper/1602.06725/) (2016)
- [Reweighted Wake-Sleep](https://mlsanity.com/paper/1406.2751/) (2014)
- [Wireless Image Retrieval at the Edge](https://mlsanity.com/paper/2007.10915/) (2020)
- [Generative Joint Source-Channel Coding for Semantic Image Transmission](https://mlsanity.com/paper/2211.13772/) (2022)
- [DeepWiVe: Deep-Learning-Aided Wireless Video Transmission](https://mlsanity.com/paper/2111.13034/) (2021)
- [Deep Joint Source-Channel Coding for Semantic Communications](https://mlsanity.com/paper/2211.08747/) (2022)
- [Rethinking Modern Communication from Semantic Coding to Semantic Communication](https://mlsanity.com/paper/2110.08496/) (2021)
- [Channel-Adaptive Wireless Image Transmission with OFDM](https://mlsanity.com/paper/2205.02417/) (2022)
- [Vector Quantized Semantic Communication System](https://mlsanity.com/paper/2209.11519/) (2022)
- [Distributed Deep Joint Source-Channel Coding over a Multiple Access Channel](https://mlsanity.com/paper/2211.09920/) (2022)
- [Deep Joint Source-Channel and Encryption Coding: Secure Semantic Communications](https://mlsanity.com/paper/2208.09245/) (2022)
- [Joint Source-Channel Coding for Channel-Adaptive Digital Semantic Communications](https://mlsanity.com/paper/2311.08146/) (2023)
- [Successive Refinement of Images with Deep Joint Source-Channel Coding](https://mlsanity.com/paper/1903.06333/) (2019)
- [TexShape: Information Theoretic Sentence Embedding for Language Models](https://mlsanity.com/paper/2402.05132/) (2024)
- [Vision Transformer for Adaptive Image Transmission over MIMO Channels](https://mlsanity.com/paper/2210.15347/) (2022)
- [Secure Deep-JSCC Against Multiple Eavesdroppers](https://mlsanity.com/paper/2308.02892/) (2023)
- [Timely and Massive Communication in 6G: Pragmatics, Learning, and Inference](https://mlsanity.com/paper/2306.17580/) (2023)
- [Deep Joint Source-Channel Coding Over Cooperative Relay Networks](https://mlsanity.com/paper/2211.06705/) (2022)

## Built on by (most cited first, 4 of 4)

- [Rateless Joint Source-Channel Coding, and a Blueprint for 6G Semantic Communications System Design](https://mlsanity.com/paper/2502.06095/) (2025)
- [UniTAC: Universal Task-Aware Compression via Weighted Distortion Measures](https://mlsanity.com/paper/2608.16696/) (2026)
- [From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity](https://mlsanity.com/paper/2609.10714/) (2026)
- [Semantic-Aware Neural Video Codec for Error-Resilient Low-Latency Transmission](https://mlsanity.com/paper/2609.16279/) (2026)

## Abstract (arXiv)

Semantic communication, when examined through the lens of joint source-channel coding (JSCC), maps source messages directly into channel input symbols, where the measure of success is defined by end-to-end distortion rather than traditional metrics such as block error rate. Previous studies have shown significant improvements achieved through deep learning (DL)-driven JSCC compared to traditional separate source and channel coding. However, JSCC is impractical in existing communication networks, where application and network providers are typically different entities connected over general-purpose TCP/IP links. In this paper, we propose designing the source and channel mappings separately and sequentially via a novel multi-level reliability interface. This conceptual interface enables semi-JSCC at both the learned source and channel mappers and achieves many of the gains observed in existing DL-based JSCC work (which would require a fully joint design between the application and the network), such as lower end-to-end distortion and graceful degradation of distortion with channel quality. We believe this work represents an important step towards realizing semantic communications in wireless networks.