Twelve Scientific Challenges for 6G: Rethinking the Foundations of Communications Theory
Not on the map. Why?
Abstract (arXiv)
The research in the sixth generation of communication networks needs to tackle new challenges in order to meet the requirements of emerging applications in terms of high data rate, low latency, high reliability, and massive connectivity. To this end, the entire communication chain needs to be optimized, including the channel and the surrounding environment, as it is no longer sufficient to control the transmitter and/or the receiver only. Investigating large intelligent surfaces, ultra massive multiple-input multiple-output, and smart constructive environments will contribute to this direction. In addition, to allow the exchange of high dimensional sensing data between connected intelligent devices, semantic and goal oriented communications need to be considered for a more efficient and context-aware information encoding. In particular, for multi-agent systems, where agents are collaborating together to achieve a complex task, emergent communications, instead of hard coded communications, can be learned for more efficient task execution and communication resources use. Moreover, new physics phenomenon should be exploited such as the thermodynamics of communication as well as the the interaction between information theory and electromagnetism to better understand the physical limitations of different technologies, e.g, holographic communications. Another new communication paradigm is to consider the end-to-end approach instead of block-by-block optimization, which requires exploiting machine learning theory, non-linear signal processing theory, and non-coherent communications theory. Within this context, we identify twelve scientific challenges for rebuilding the theoretical foundations of communications, and we overview each of the challenges while providing research opportunities and open questions for the research community.
Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jul 2022.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 1; 2023: 9; 2024: 1; 2025: 2; 2026: 2.
15 papers.
Sort:
By area:
- 2023Large Generative AI Models for Telecom: The Next Big Thing?cited by 52
- 2023Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communicationscited by 10
- 2023Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networkscited by 9
- 2023The Role of Federated Learning in a Wireless World with Foundation Modelscited by 7
- 2024Telecom Foundation Models: Applications, Challenges, and Future Trendscited by 6
- 2022The Interplay of AI and Digital Twin: Bridging the Gap between Data-Driven and Model-Driven Approachescited by 4
- 2023Probabilistic Constellation Shaping With Denoising Diffusion Probabilistic Models: A Novel Approachcited by 4
- 2023Deep Learning Based Channel Estimation in High Mobility Communications Using Bi-RNN Networkscited by 3
- 2023Generative AI-Based Probabilistic Constellation Shaping With Diffusion Modelscited by 2
- 2025Online Learning-based Adaptive Beam Switching for 6G Networks: Enhancing Efficiency and Resiliencecited by 1
- 2026Equivariant Multi-agent Reinforcement Learning for Multimodal Vehicle-to-Infrastructure Systemscited by 1
- 2023An ML-assisted OTFS vs. OFDM adaptable modemcited by 0
3 more
- 2023Denoising Diffusion Probabilistic Models for Hardware-Impaired Communicationscited by 0
- 2025GFlowNets for Active Learning Based Resource Allocation in Next Generation Wireless Networkscited by 0
- 2026From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivitycited by 0
- 2026From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity9 Sep 2026
- 2026Equivariant Multi-agent Reinforcement Learning for Multimodal Vehicle-to-Infrastructure Systems8 Apr 2026
- 2025Online Learning-based Adaptive Beam Switching for 6G Networks: Enhancing Efficiency and Resilience12 May 2025
- 2025GFlowNets for Active Learning Based Resource Allocation in Next Generation Wireless Networks8 May 2025
- 2024Telecom Foundation Models: Applications, Challenges, and Future Trends2 Aug 2024
- 2023Generative AI-Based Probabilistic Constellation Shaping With Diffusion Models15 Nov 2023
- 2023Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communications30 Oct 2023
- 2023The Role of Federated Learning in a Wireless World with Foundation Models6 Oct 2023
- 2023Probabilistic Constellation Shaping With Denoising Diffusion Probabilistic Models: A Novel Approach15 Sep 2023
- 2023Denoising Diffusion Probabilistic Models for Hardware-Impaired Communications15 Sep 2023
- 2023Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks12 Sep 2023
- 2023An ML-assisted OTFS vs. OFDM adaptable modem4 Sep 2023
3 more
- 2023Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communicationscited by 10
- 2023Probabilistic Constellation Shaping With Denoising Diffusion Probabilistic Models: A Novel Approachcited by 4
- 2023Generative AI-Based Probabilistic Constellation Shaping With Diffusion Modelscited by 2
- 2023Denoising Diffusion Probabilistic Models for Hardware-Impaired Communicationscited by 0
- 2023Generative AI-Based Probabilistic Constellation Shaping With Diffusion Models15 Nov 2023
- 2023Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communications30 Oct 2023
- 2023Probabilistic Constellation Shaping With Denoising Diffusion Probabilistic Models: A Novel Approach15 Sep 2023
- 2023Denoising Diffusion Probabilistic Models for Hardware-Impaired Communications15 Sep 2023
- 2023Large Generative AI Models for Telecom: The Next Big Thing?cited by 52
- 2023Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networkscited by 9
- 2023The Role of Federated Learning in a Wireless World with Foundation Modelscited by 7
- 2024Telecom Foundation Models: Applications, Challenges, and Future Trendscited by 6
- 2022The Interplay of AI and Digital Twin: Bridging the Gap between Data-Driven and Model-Driven Approachescited by 4
- 2023Deep Learning Based Channel Estimation in High Mobility Communications Using Bi-RNN Networkscited by 3
- 2025Online Learning-based Adaptive Beam Switching for 6G Networks: Enhancing Efficiency and Resiliencecited by 1
- 2026Equivariant Multi-agent Reinforcement Learning for Multimodal Vehicle-to-Infrastructure Systemscited by 1
- 2023An ML-assisted OTFS vs. OFDM adaptable modemcited by 0
- 2025GFlowNets for Active Learning Based Resource Allocation in Next Generation Wireless Networkscited by 0
- 2026From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivitycited by 0
- 2026From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity9 Sep 2026
- 2026Equivariant Multi-agent Reinforcement Learning for Multimodal Vehicle-to-Infrastructure Systems8 Apr 2026
- 2025Online Learning-based Adaptive Beam Switching for 6G Networks: Enhancing Efficiency and Resilience12 May 2025
- 2025GFlowNets for Active Learning Based Resource Allocation in Next Generation Wireless Networks8 May 2025
- 2024Telecom Foundation Models: Applications, Challenges, and Future Trends2 Aug 2024
- 2023The Role of Federated Learning in a Wireless World with Foundation Models6 Oct 2023
- 2023Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks12 Sep 2023
- 2023An ML-assisted OTFS vs. OFDM adaptable modem4 Sep 2023
- 2023Large Generative AI Models for Telecom: The Next Big Thing?17 Jun 2023
- 2023Deep Learning Based Channel Estimation in High Mobility Communications Using Bi-RNN Networks29 Apr 2023
- 2022The Interplay of AI and Digital Twin: Bridging the Gap between Data-Driven and Model-Driven Approaches26 Sep 2022