# Learning Semantics: An Opportunity for Effective 6G Communications

arXiv: https://arxiv.org/abs/2110.08049 · HTML: https://mlsanity.com/paper/2110.08049/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 14 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [MINE: Mutual Information Neural Estimation](https://mlsanity.com/paper/1801.04062/) (2018)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications](https://mlsanity.com/paper/2011.14844/) (2020)
- [Semantics-Empowered Communication for Networked Intelligent Systems](https://mlsanity.com/paper/2007.11579/) (2020)
- [6G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directions](https://mlsanity.com/paper/1909.11315/) (2019)
- [Deep Source-Channel Coding for Sentence Semantic Transmission with HARQ](https://mlsanity.com/paper/2106.03009/) (2021)
- [Semantic Communications for Speech Signals](https://mlsanity.com/paper/2012.05369/) (2020)
- [A new communication paradigm: from bit accuracy to semantic fidelity](https://mlsanity.com/paper/2101.12649/) (2021)

## Built on by (most cited first, 20 of 20)

- [Semantic Communications: Principles and Challenges](https://mlsanity.com/paper/2201.01389/) (2021)
- [Semantic Channel Equalizer: Modelling Language Mismatch in Multi-User Semantic Communications](https://mlsanity.com/paper/2308.03789/) (2023)
- [Wireless End-to-End Image Transmission System using Semantic Communications](https://mlsanity.com/paper/2302.13721/) (2023)
- [Semantic Information Recovery in Wireless Networks](https://mlsanity.com/paper/2204.13366/) (2022)
- [Semantics-Empowered Communication: A Tutorial-cum-Survey](https://mlsanity.com/paper/2212.08487/) (2022)
- [Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization](https://mlsanity.com/paper/2411.19719/) (2024)
- [Goal-Oriented Quantization: Analysis, Design, and Application to Resource Allocation](https://mlsanity.com/paper/2209.15347/) (2022)
- [How to Evaluate Semantic Communications for Images with ViTScore Metric?](https://mlsanity.com/paper/2309.04891/) (2023)
- [Adaptive Information Bottleneck Guided Joint Source and Channel Coding for Image Transmission](https://mlsanity.com/paper/2203.06492/) (2022)
- [Reasoning with the Theory of Mind for Pragmatic Semantic Communication](https://mlsanity.com/paper/2311.18224/) (2023)
- [Semantic Communication for Cooperative Multi-Task Processing over Wireless Networks](https://mlsanity.com/paper/2404.08483/) (2024)
- [Semantic Communication Enhanced by Knowledge Graph Representation Learning](https://mlsanity.com/paper/2407.19338/) (2024)
- [Cooperative and Collaborative Multi-Task Semantic Communication for Distributed Sources](https://mlsanity.com/paper/2411.02150/) (2024)
- [Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges](https://mlsanity.com/paper/2411.18199/) (2024)
- [Semantic Revolution from Communications to Orchestration for 6G: Challenges, Enablers, and Research Directions](https://mlsanity.com/paper/2407.00081/) (2024)
- [On-Air Deep Learning Integrated Semantic Inference Models for Enhanced Earth Observation Satellite Networks](https://mlsanity.com/paper/2409.15246/) (2024)
- [Soft Partitioning of Latent Space for Semantic Channel Equalization](https://mlsanity.com/paper/2405.20085/) (2024)
- [RIS-aided Latent Space Alignment for Semantic Channel Equalization](https://mlsanity.com/paper/2507.16450/) (2025)
- [Semantic Waveforms for AI-Native 6G Networks](https://mlsanity.com/paper/2602.13316/) (2026)
- [AeroLat: Channel-Aware Latent Space Semantic Communication for Decentralized UAV Swarms](https://mlsanity.com/paper/2609.16947/) (2026)

## Abstract (arXiv)

Recently, semantic communications are envisioned as a key enabler of future 6G networks. Back to Shannon's information theory, the goal of communication has long been to guarantee the correct reception of transmitted messages irrespective of their meaning. However, in general, whenever communication occurs to convey a meaning, what matters is the receiver's understanding of the transmitted message and not necessarily its correct reconstruction. Hence, semantic communications introduce a new paradigm: transmitting only relevant information sufficient for the receiver to capture the meaning intended can save significant communication bandwidth. Thus, this work explores the opportunity offered by semantic communications for beyond 5G networks. In particular, we focus on the benefit of semantic compression. We refer to semantic message as a sequence of well-formed symbols learned from the "meaning" underlying data, which have to be interpreted at the receiver. This requires a reasoning unit, here artificial, on a knowledge base: a symbolic knowledge representation of the specific application. Therefore, we present and detail a novel architecture that enables representation learning of semantic symbols for effective semantic communications. We first discuss theoretical aspects and successfully design objective functions, which help learn effective semantic encoders and decoders. Eventually, we show promising numerical results for the scenario of text transmission, especially when the sender and receiver speak different languages.