Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges
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Abstract (arXiv)
The fifth generation (5G) wireless communication networks are currently being deployed, and beyond 5G (B5G) networks are expected to be developed over the next decade. Artificial intelligence (AI) technologies and, in particular, machine learning (ML) have the potential to efficiently solve the unstructured and seemingly intractable problems by involving large amounts of data that need to be dealt with in B5G. This article studies how AI and ML can be leveraged for the design and operation of B5G networks. We first provide a comprehensive survey of recent advances and future challenges that result from bringing AI/ML technologies into B5G wireless networks. Our survey touches different aspects of wireless network design and optimization, including channel measurements, modeling, and estimation, physical-layer research, and network management and optimization. Then, ML algorithms and applications to B5G networks are reviewed, followed by an overview of standard developments of applying AI/ML algorithms to B5G networks. We conclude this study by the future challenges on applying AI/ML to B5G networks.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Jan 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 5; 2024: 1.
9 papers.
- 2023Holistic Network Virtualization and Pervasive Network Intelligence for 6Gcited by 14
- 2024On the Combination of AI and Wireless Technologies: 3GPP Standardization Progresscited by 3
- 2021Learning Autonomy in Management of Wireless Random Networkscited by 2
- 2022Defensive Distillation based Adversarial Attacks Mitigation Method for Channel Estimation using Deep Learning Models in Next-Generation Wireless Networkscited by 2
- 2023Low PAPR MIMO-OFDM Design Based on Convolutional Autoencodercited by 2
- 2023Unsupervised ANN-Based Equalizer and Its Trainable FPGA Implementationcited by 2
- 2023Federated Learning over a Wireless Network: Distributed User Selection through Random Accesscited by 2
- 2022Multi-Agent Feedback Enabled Neural Networks for Intelligent Communicationscited by 1
- 2023Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systemscited by 1
- 2024On the Combination of AI and Wireless Technologies: 3GPP Standardization Progress17 Jun 2024
- 2023Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems1 Nov 2023
- 2023Federated Learning over a Wireless Network: Distributed User Selection through Random Access7 Jul 2023
- 2023Unsupervised ANN-Based Equalizer and Its Trainable FPGA Implementation14 Apr 2023
- 2023Low PAPR MIMO-OFDM Design Based on Convolutional Autoencoder11 Jan 2023
- 2023Holistic Network Virtualization and Pervasive Network Intelligence for 6G2 Jan 2023
- 2022Defensive Distillation based Adversarial Attacks Mitigation Method for Channel Estimation using Deep Learning Models in Next-Generation Wireless Networks12 Aug 2022
- 2022Multi-Agent Feedback Enabled Neural Networks for Intelligent Communications22 May 2022
- 2021Learning Autonomy in Management of Wireless Random Networks15 Jun 2021