# Semantic Information Recovery in Wireless Networks arXiv: https://arxiv.org/abs/2204.13366 ยท HTML: https://mlsanity.com/paper/2204.13366/ ## Key facts - Cited by 12 papers in the map (data through 14 Oct 2026). - Builds on 24 of its 58 references that are in the map. - First posted on arXiv 28 Apr 2022. ## Builds on (most cited first, 24 of 24) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015) - [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016) - [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016) - [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020) - [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017) - [Sionna: An Open-Source Library for Next-Generation Physical Layer Research](https://mlsanity.com/paper/2203.11854/) (2022) - [6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications](https://mlsanity.com/paper/2011.14844/) (2020) - [Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach](https://mlsanity.com/paper/2102.04170/) (2021) - [Task-Oriented Multi-User Semantic Communications](https://mlsanity.com/paper/2112.10255/) (2021) - [Deep Learning for Joint Source-Channel Coding of Text](https://mlsanity.com/paper/1802.06832/) (2018) - [The Information Bottleneck Problem and Its Applications in Machine Learning](https://mlsanity.com/paper/2004.14941/) (2020) - [Task-Oriented Communication for Multi-Device Cooperative Edge Inference](https://mlsanity.com/paper/2109.00172/) (2021) - [What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence](https://mlsanity.com/paper/2110.00196/) (2021) - [A Very Brief Introduction to Machine Learning With Applications to Communication Systems](https://mlsanity.com/paper/1808.02342/) (2018) - [Distributed Variational Representation Learning](https://mlsanity.com/paper/1807.04193/) (2018) - [On the Information Bottleneck Problems: Models, Connections, Applications and Information Theoretic Views](https://mlsanity.com/paper/2002.00008/) (2020) - [Semantic Communications in Networked Systems: A Data Significance Perspective](https://mlsanity.com/paper/2103.05391/) (2021) - [A Brief Introduction to Machine Learning for Engineers](https://mlsanity.com/paper/1709.02840/) (2017) - [Semantic-Effectiveness Filtering and Control for Post-5G Wireless Connectivity](https://mlsanity.com/paper/1907.02441/) (2019) - [Learning Semantics: An Opportunity for Effective 6G Communications](https://mlsanity.com/paper/2110.08049/) (2021) - [Semantic Communications for Speech Signals](https://mlsanity.com/paper/2012.05369/) (2020) - [Data-Driven Symbol Detection via Model-Based Machine Learning](https://mlsanity.com/paper/2002.07806/) (2020) - [CMDNet: Learning a Probabilistic Relaxation of Discrete Variables for Soft Detection with Low Complexity](https://mlsanity.com/paper/2102.12756/) (2021) ## Built on by (most cited first, 12 of 12) - [Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach](https://mlsanity.com/paper/2304.12502/) (2023) - [Effective Communication with Dynamic Feature Compression](https://mlsanity.com/paper/2401.16236/) (2024) - [Semantic Communication for Cooperative Multi-Task Processing over Wireless Networks](https://mlsanity.com/paper/2404.08483/) (2024) - [Cooperative and Collaborative Multi-Task Semantic Communication for Distributed Sources](https://mlsanity.com/paper/2411.02150/) (2024) - [Model-free Reinforcement Learning of Semantic Communication by Stochastic Policy Gradient](https://mlsanity.com/paper/2305.03571/) (2023) - [RIS-aided Latent Space Alignment for Semantic Channel Equalization](https://mlsanity.com/paper/2507.16450/) (2025) - [Dynamic Encoding and Decoding of Information for Split Learning in Mobile-Edge Computing: Leveraging Information Bottleneck Theory](https://mlsanity.com/paper/2309.02787/) (2023) - [Diffusion-aided Task-oriented Semantic Communications with Model Inversion Attack](https://mlsanity.com/paper/2506.19886/) (2025) - [Semantic-Aware Task Clustering for Federated Cooperative Multi-Task Semantic Communication](https://mlsanity.com/paper/2601.17419/) (2026) - [Integrating Semantic Communication and Human Decision-Making into an End-to-End Sensing-Decision Framework](https://mlsanity.com/paper/2412.05103/) (2024) - [Transferable Deployment of Semantic Edge Inference Systems via Unsupervised Domain Adaption](https://mlsanity.com/paper/2504.11873/) (2025) - [CST: Collaborative Selective Transmission for Communication-Efficient Multimodal Edge Inference](https://mlsanity.com/paper/2608.22115/) (2026) ## Abstract (arXiv) Motivated by the recent success of Machine Learning (ML) tools in wireless communications, the idea of semantic communication by Weaver from 1949 has gained attention. It breaks with Shannon's classic design paradigm by aiming to transmit the meaning of a message, i.e., semantics, rather than its exact version and thus allows for savings in information rate. In this work, we extend the fundamental approach from Basu et al. for modeling semantics to the complete communications Markov chain. Thus, we model semantics by means of hidden random variables and define the semantic communication task as the data-reduced and reliable transmission of messages over a communication channel such that semantics is best preserved. We cast this task as an end-to-end Information Bottleneck problem, allowing for compression while preserving relevant information most. As a solution approach, we propose the ML-based semantic communication system SINFONY and use it for a distributed multipoint scenario: SINFONY communicates the meaning behind multiple messages that are observed at different senders to a single receiver for semantic recovery. We analyze SINFONY by processing images as message examples. Numerical results reveal a tremendous rate-normalized SNR shift up to 20 dB compared to classically designed communication systems.