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About this Research Topic

Manuscript Submission Deadline 13 November 2023
Manuscript Extension Submission Deadline 31 January 2024

The wireless network industry is advancing towards 6G, which promises new applications that were not possible before. Machine learning (ML) is expected to play a critical role in the development of 6G, as it can help improve network performance and enable new use cases. Recent advances in ML have already demonstrated its potential for 6G, such as enhancing spectrum efficiency, network security, and quality-of-service (QoS) provisioning. However, there are still many open issues that need to be addressed, such as designing ML algorithms that are scalable, efficient, and robust to different network conditions. Overall, ML is expected to be a key enabler for 6G, and research in this area is essential to realizing the full potential of the new wireless network generation.

This Research Topic aims to explore the potential of ML for 6G and identify the challenges and opportunities associated with its implementation. Specifically, we aim to address the key use cases for ML in 6G, the role of ML in improving the performance of 6G networks, the challenges associated with integrating ML into 6G networks, and the ethical considerations that need to be taken into account when using ML in 6G.

We aim to bring together recent advances in ML for 6G communication networks. The collection solicits research papers that address the following non-exhaustive list of topics:

• Machine learning for 6G Physical layer and transceiver design
• Machine learning for 6G resource allocation and multiple access
• Distributed machine learning for 6G networks
• Deep reinforcement learning for wireless communications
• Machine learning for network orchestration
• Machine learning testbed and experiment
• Machine learning for 6G applications and use cases
• Performance evaluation of machine learning-enabled 6G networks
• Security and privacy issues related to machine learning in 6G

We are interested in original research articles, reviews, and perspectives that provide insights into state-of-the-art ML for 6G. All manuscripts will be subject to a rigorous peer-review process to ensure high quality and relevance to the research topic. Our aim is to provide a comprehensive overview of the current state-of-the-art and future directions of ML for 6G, and we believe that this Research Topic will be of interest to researchers, practitioners, and policymakers in the field of wireless communications.

Keywords: Machine learning, 6G, Communication networks, Resource allocation, Multiple access, Deep reinforcement learning, Wireless communications, 6G applications, Security and Privacy in 6G, 6G architectures


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

The wireless network industry is advancing towards 6G, which promises new applications that were not possible before. Machine learning (ML) is expected to play a critical role in the development of 6G, as it can help improve network performance and enable new use cases. Recent advances in ML have already demonstrated its potential for 6G, such as enhancing spectrum efficiency, network security, and quality-of-service (QoS) provisioning. However, there are still many open issues that need to be addressed, such as designing ML algorithms that are scalable, efficient, and robust to different network conditions. Overall, ML is expected to be a key enabler for 6G, and research in this area is essential to realizing the full potential of the new wireless network generation.

This Research Topic aims to explore the potential of ML for 6G and identify the challenges and opportunities associated with its implementation. Specifically, we aim to address the key use cases for ML in 6G, the role of ML in improving the performance of 6G networks, the challenges associated with integrating ML into 6G networks, and the ethical considerations that need to be taken into account when using ML in 6G.

We aim to bring together recent advances in ML for 6G communication networks. The collection solicits research papers that address the following non-exhaustive list of topics:

• Machine learning for 6G Physical layer and transceiver design
• Machine learning for 6G resource allocation and multiple access
• Distributed machine learning for 6G networks
• Deep reinforcement learning for wireless communications
• Machine learning for network orchestration
• Machine learning testbed and experiment
• Machine learning for 6G applications and use cases
• Performance evaluation of machine learning-enabled 6G networks
• Security and privacy issues related to machine learning in 6G

We are interested in original research articles, reviews, and perspectives that provide insights into state-of-the-art ML for 6G. All manuscripts will be subject to a rigorous peer-review process to ensure high quality and relevance to the research topic. Our aim is to provide a comprehensive overview of the current state-of-the-art and future directions of ML for 6G, and we believe that this Research Topic will be of interest to researchers, practitioners, and policymakers in the field of wireless communications.

Keywords: Machine learning, 6G, Communication networks, Resource allocation, Multiple access, Deep reinforcement learning, Wireless communications, 6G applications, Security and Privacy in 6G, 6G architectures


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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