# Semantic Communications Based on Adaptive Generative Models and Information Bottleneck

arXiv: https://arxiv.org/abs/2309.02387 · HTML: https://mlsanity.com/paper/2309.02387/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2023.

## Built on by (most cited first, 16 of 16)

- [Position: Topological Deep Learning is the New Frontier for Relational Learning](https://mlsanity.com/paper/2402.08871/) (2024)
- [Diffusion models for audio semantic communication](https://mlsanity.com/paper/2309.07195/) (2023)
- [Language-Oriented Communication with Semantic Coding and Knowledge Distillation for Text-to-Image Generation](https://mlsanity.com/paper/2309.11127/) (2023)
- [Dynamic Relative Representations for Goal-Oriented Semantic Communications](https://mlsanity.com/paper/2403.16986/) (2024)
- [Language-Oriented Semantic Latent Representation for Image Transmission](https://mlsanity.com/paper/2405.09976/) (2024)
- [Generative AI Meets Semantic Communication: Evolution and Revolution of Communication Tasks](https://mlsanity.com/paper/2401.06803/) (2024)
- [Lightweight Diffusion Models for Resource-Constrained Semantic Communication](https://mlsanity.com/paper/2410.02491/) (2024)
- [Large Language Models (LLMs) for Semantic Communication in Edge-based IoT Networks](https://mlsanity.com/paper/2407.20970/) (2024)
- [Latent Space Alignment for AI-Native MIMO Semantic Communications](https://mlsanity.com/paper/2507.16680/) (2025)
- [On-Air Deep Learning Integrated Semantic Inference Models for Enhanced Earth Observation Satellite Networks](https://mlsanity.com/paper/2409.15246/) (2024)
- [SQ-GAN: Semantic Image Communications Using Masked Vector Quantization](https://mlsanity.com/paper/2502.09520/) (2025)
- [Multi-Task Semantic Communications via Large Models](https://mlsanity.com/paper/2503.22064/) (2025)
- [On the Rate-Distortion-Complexity Tradeoff for Semantic Communication](https://mlsanity.com/paper/2602.14481/) (2026)
- [Federated Latent Space Alignment for Multi-user Semantic Communications](https://mlsanity.com/paper/2602.17271/) (2026)
- [A Survey on Semantic Communication for Vision: Categories, Frameworks, Enabling Techniques, and Applications](https://mlsanity.com/paper/2601.22202/) (2026)
- [Sheaf-Based Federated Representation Learning](https://mlsanity.com/paper/2608.10016/) (2026)

## Abstract (arXiv)

Semantic communications represent a significant breakthrough with respect to the current communication paradigm, as they focus on recovering the meaning behind the transmitted sequence of symbols, rather than the symbols themselves. In semantic communications, the scope of the destination is not to recover a list of symbols symbolically identical to the transmitted ones, but rather to recover a message that is semantically equivalent to the semantic message emitted by the source. This paradigm shift introduces many degrees of freedom to the encoding and decoding rules that can be exploited to make the design of communication systems much more efficient. In this paper, we present an approach to semantic communication building on three fundamental ideas: 1) represent data over a topological space as a formal way to capture semantics, as expressed through relations; 2) use the information bottleneck principle as a way to identify relevant information and adapt the information bottleneck online, as a function of the wireless channel state, in order to strike an optimal trade-off between transmit power, reconstruction accuracy and delay; 3) exploit probabilistic generative models as a general tool to adapt the transmission rate to the wireless channel state and make possible the regeneration of the transmitted images or run classification tasks at the receiver side.