# Towards 6G Networks: Use Cases and Technologies

arXiv: https://arxiv.org/abs/1903.12216 · HTML: https://mlsanity.com/paper/1903.12216/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2019.

## Built on by (most cited first, 14 of 14)

- [AI-Native Network Slicing for 6G Networks](https://mlsanity.com/paper/2105.08576/) (2021)
- [An Energy and Carbon Footprint Analysis of Distributed and Federated Learning](https://mlsanity.com/paper/2206.10380/) (2022)
- [On the Convergence Time of Federated Learning Over Wireless Networks Under Imperfect CSI](https://mlsanity.com/paper/2104.00331/) (2021)
- [Towards Supporting Intelligence in 5G/6G Core Networks: NWDAF Implementation and Initial Analysis](https://mlsanity.com/paper/2205.15121/) (2022)
- [Goal-Oriented Quantization: Analysis, Design, and Application to Resource Allocation](https://mlsanity.com/paper/2209.15347/) (2022)
- [Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges](https://mlsanity.com/paper/2112.04698/) (2021)
- [A Reinforcement Learning Framework for PQoS in a Teleoperated Driving Scenario](https://mlsanity.com/paper/2202.01949/) (2022)
- [Predictive Quality of Service (PQoS): The Next Frontier for Fully Autonomous Systems](https://mlsanity.com/paper/2109.09376/) (2021)
- [Hybrid-Layers Neural Network Architectures for Modeling the Self-Interference in Full-Duplex Systems](https://mlsanity.com/paper/2110.09997/) (2021)
- [The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining](https://mlsanity.com/paper/2202.08185/) (2022)
- [Artificial Intelligence in Vehicular Wireless Networks: A Case Study Using ns-3](https://mlsanity.com/paper/2203.05449/) (2022)
- [Distributed Resource Allocation for URLLC in IIoT Scenarios: A Multi-Armed Bandit Approach](https://mlsanity.com/paper/2211.12201/) (2022)
- [Beam Management with Orientation and RSRP using Deep Learning for Beyond 5G Systems](https://mlsanity.com/paper/2202.02247/) (2022)
- [An Unsupervised Learning Approach for Spectrum Allocation in Terahertz Communication Systems](https://mlsanity.com/paper/2208.03618/) (2022)

## Abstract (arXiv)

Reliable data connectivity is vital for the ever increasingly intelligent, automated and ubiquitous digital world. Mobile networks are the data highways and, in a fully connected, intelligent digital world, will need to connect everything, from people to vehicles, sensors, data, cloud resources and even robotic agents. Fifth generation (5G) wireless networks (that are being currently deployed) offer significant advances beyond LTE, but may be unable to meet the full connectivity demands of the future digital society. Therefore, this article discusses technologies that will evolve wireless networks towards a sixth generation (6G), and that we consider as enablers for several potential 6G use cases. We provide a full-stack, system-level perspective on 6G scenarios and requirements, and select 6G technologies that can satisfy them either by improving the 5G design, or by introducing completely new communication paradigms.