# Less Data, More Knowledge: Building Next Generation Semantic Communication Networks

arXiv: https://arxiv.org/abs/2211.14343 · HTML: https://mlsanity.com/paper/2211.14343/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 98 references that are in the map.
- First posted on arXiv 25 Nov 2022.

## Builds on (most cited first, 30 of 40)

- [Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation](https://mlsanity.com/paper/2106.04399/) (2021)
- [Debiased Contrastive Learning](https://mlsanity.com/paper/2007.00224/) (2020)
- [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)
- [Representation Learning via Invariant Causal Mechanisms](https://mlsanity.com/paper/2010.07922/) (2020)
- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications](https://mlsanity.com/paper/2011.14844/) (2020)
- [Causal Machine Learning: A Survey and Open Problems](https://mlsanity.com/paper/2206.15475/) (2022)
- [Semantic Communications: Principles and Challenges](https://mlsanity.com/paper/2201.01389/) (2021)
- [Nonlinear Transform Source-Channel Coding for Semantic Communications](https://mlsanity.com/paper/2112.10961/) (2021)
- [Deep Learning-Based Channel Estimation](https://mlsanity.com/paper/1810.05893/) (2018)
- [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)
- [Toward a 6G AI-Native Air Interface](https://mlsanity.com/paper/2012.08285/) (2020)
- [Domain Invariant Representation Learning with Domain Density Transformations](https://mlsanity.com/paper/2102.05082/) (2021)
- [Task-Oriented Multi-User Semantic Communications for VQA Task](https://mlsanity.com/paper/2108.07357/) (2021)
- [What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence](https://mlsanity.com/paper/2110.00196/) (2021)
- [From Statistical to Causal Learning](https://mlsanity.com/paper/2204.00607/) (2022)
- [The Information Complexity of Learning Tasks, their Structure and their Distance](https://mlsanity.com/paper/1904.03292/) (2019)
- [AI-Native Network Slicing for 6G Networks](https://mlsanity.com/paper/2105.08576/) (2021)
- [Performance Optimization for Semantic Communications: An Attention-based Reinforcement Learning Approach](https://mlsanity.com/paper/2208.08239/) (2022)
- [ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection](https://mlsanity.com/paper/1905.10750/) (2019)
- [Semantic Communications in Networked Systems: A Data Significance Perspective](https://mlsanity.com/paper/2103.05391/) (2021)
- [Rethinking Modern Communication from Semantic Coding to Semantic Communication](https://mlsanity.com/paper/2110.08496/) (2021)
- [Semantic Communication with Adaptive Universal Transformer](https://mlsanity.com/paper/2108.09119/) (2021)
- [Neuro-Symbolic Causal Reasoning Meets Signaling Game for Emergent Semantic Communications](https://mlsanity.com/paper/2210.12040/) (2022)
- [Semantic Communications for Speech Signals](https://mlsanity.com/paper/2012.05369/) (2020)
- [Intelligent O-RAN for Beyond 5G and 6G Wireless Networks](https://mlsanity.com/paper/2005.08374/) (2020)
- [Curriculum Learning for Goal-Oriented Semantic Communications with a Common Language](https://mlsanity.com/paper/2204.10429/) (2022)
- [Edge Continual Learning for Dynamic Digital Twins over Wireless Networks](https://mlsanity.com/paper/2204.04795/) (2022)

## Built on by (most cited first, 30 of 62)

- [Large Language Models Empowered Autonomous Edge AI for Connected Intelligence](https://mlsanity.com/paper/2307.02779/) (2023)
- [Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G](https://mlsanity.com/paper/2405.02336/) (2024)
- [Wireless Multi-Agent Generative AI: From Connected Intelligence to Collective Intelligence](https://mlsanity.com/paper/2307.02757/) (2023)
- [Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks](https://mlsanity.com/paper/2309.13223/) (2023)
- [Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach](https://mlsanity.com/paper/2304.12502/) (2023)
- [Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/2405.20024/) (2024)
- [Semantic Successive Refinement: A Generative AI-aided Semantic Communication Framework](https://mlsanity.com/paper/2408.05112/) (2024)
- [Optimization of Image Transmission in a Cooperative Semantic Communication Networks](https://mlsanity.com/paper/2301.00433/) (2023)
- [Toward 6G Native-AI Network: Foundation Model based Cloud-Edge-End Collaboration Framework](https://mlsanity.com/paper/2310.17471/) (2023)
- [Joint Sensing and Semantic Communications with Multi-Task Deep Learning](https://mlsanity.com/paper/2311.05017/) (2023)
- ["X of Information'' Continuum: A Survey on AI-Driven Multi-dimensional Metrics for Next-Generation Networked Systems](https://mlsanity.com/paper/2507.19657/) (2025)
- [Adaptive Token Merging for Efficient Transformer Semantic Communication at the Edge](https://mlsanity.com/paper/2509.09955/) (2025)
- [Performance Limits of a Deep Learning-Enabled Text Semantic Communication under Interference](https://mlsanity.com/paper/2302.14702/) (2023)
- [The Seven Worlds and Experiences of the Wireless Metaverse: Challenges and Opportunities](https://mlsanity.com/paper/2304.10282/) (2023)
- [A Semantic-Aware Multiple Access Scheme for Distributed, Dynamic 6G-Based Applications](https://mlsanity.com/paper/2401.06308/) (2024)
- [Multi-Receiver Task-Oriented Communications via Multi-Task Deep Learning](https://mlsanity.com/paper/2308.06884/) (2023)
- [Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks](https://mlsanity.com/paper/2311.14469/) (2023)
- [Reasoning with the Theory of Mind for Pragmatic Semantic Communication](https://mlsanity.com/paper/2311.18224/) (2023)
- [Diff-GO: Diffusion Goal-Oriented Communications to Achieve Ultra-High Spectrum Efficiency](https://mlsanity.com/paper/2312.02984/) (2023)
- [AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges](https://mlsanity.com/paper/2501.14970/) (2025)
- [Low-Complexity Semantic Packet Aggregation for Token Communication via Lookahead Search](https://mlsanity.com/paper/2506.19451/) (2025)
- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [Internet of Federated Digital Twins (IoFDT): Connecting Twins Beyond Borders for Society 5.0](https://mlsanity.com/paper/2312.06432/) (2023)
- [The Energy Cost of Artificial Intelligence Lifecycle in Communication Networks](https://mlsanity.com/paper/2408.00540/) (2024)
- [Bridging Physical and Digital Worlds: Embodied Large AI for Future Wireless Systems](https://mlsanity.com/paper/2506.24009/) (2025)
- [Collaborative Learning with a Drone Orchestrator](https://mlsanity.com/paper/2303.02266/) (2023)
- [Joint Sensing and Task-Oriented Communications with Image and Wireless Data Modalities for Dynamic Spectrum Access](https://mlsanity.com/paper/2312.13931/) (2023)
- [Reliable Beamforming at Terahertz Bands: Are Causal Representations the Way Forward?](https://mlsanity.com/paper/2303.08017/) (2023)
- [Decentralized Semantic Traffic Control in AVs Using RL and DQN for Dynamic Roadblocks](https://mlsanity.com/paper/2406.18741/) (2024)
- [Resource-Efficient Multiview Perception: Integrating Semantic Masking with Masked Autoencoders](https://mlsanity.com/paper/2410.04817/) (2024)

## Abstract (arXiv)

Semantic communication is viewed as a revolutionary paradigm that can potentially transform how we design and operate wireless communication systems. However, despite a recent surge of research activities in this area, the research landscape remains limited. In this tutorial, we present the first rigorous vision of a scalable end-to-end semantic communication network that is founded on novel concepts from artificial intelligence (AI), causal reasoning, and communication theory. We first discuss how the design of semantic communication networks requires a move from data-driven networks towards knowledge-driven ones. Subsequently, we highlight the necessity of creating semantic representations of data that satisfy the key properties of minimalism, generalizability, and efficiency so as to do more with less. We then explain how those representations can form the basis a so-called semantic language. By using semantic representation and languages, we show that the traditional transmitter and receiver now become a teacher and apprentice. Then, we define the concept of reasoning by investigating the fundamentals of causal representation learning and their role in designing semantic communication networks. We demonstrate that reasoning faculties are majorly characterized by the ability to capture causal and associational relationships in datastreams. For such reasoning-driven networks, we propose novel and essential semantic communication metrics that include new "reasoning capacity" measures that could go beyond Shannon's bound to capture the convergence of computing and communication. Finally, we explain how semantic communications can be scaled to large-scale networks (6G and beyond). In a nutshell, we expect this tutorial to provide a comprehensive reference on how to properly build, analyze, and deploy future semantic communication networks.