Inaugural Editorial
I am delighted to introduce the inaugural volume of the IEEE Transactions on Machine Learning in Communications and Networking (TMLCN), which is an interdisciplinary journal supported by the IEEE Communications Society, the IEEE Signal Processing Society, the IEEE Vehicular Technology Society, and t...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
| Online Access: | https://ieeexplore.ieee.org/document/10116055/ |
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| author | Walid Saad |
| author_facet | Walid Saad |
| author_sort | Walid Saad |
| collection | DOAJ |
| description | I am delighted to introduce the inaugural volume of the IEEE Transactions on Machine Learning in Communications and Networking (TMLCN), which is an interdisciplinary journal supported by the IEEE Communications Society, the IEEE Signal Processing Society, the IEEE Vehicular Technology Society, and the IEEE Computer Society. The relationship between communication systems and artificial intelligence (AI) is not new, and it has gone through multiple phases over the past couple of decades. Early on, researchers working on communication and networking technologies, such as wireless systems, viewed AI as an unnecessary, and unreliable tool (e.g., compared to the rigor of communication theory and information theory) that could never play any role in the design of communication systems. Subsequently, there was a renewed surge of interest in AI during the incipient stages of so-called cognitive radio networks. At the time, AI appeared to have a potentially important role in the search for dynamic spectrum sharing and access techniques. However, the complexity of AI tools such as neural networks and the shortage of computing resources at the time, meant that those efforts had very limited success. However, the dawn of deep learning architectures, coupled with the recent leap in AI technologies propelled by the advent of large language models (LLMs), has unequivocally transformed the AI landscape, paving the way for AI to become an indispensable framework for designing present and forthcoming communication and networking systems. This is corroborated by several current initiatives across academia, industry, and the government that are looking at what a future “AI-native” wireless system could look like. |
| format | Article |
| id | doaj-art-71e15af324c0445d85f627a18944ec73 |
| institution | DOAJ |
| issn | 2831-316X |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Machine Learning in Communications and Networking |
| spelling | doaj-art-71e15af324c0445d85f627a18944ec732025-08-20T02:53:07ZengIEEEIEEE Transactions on Machine Learning in Communications and Networking2831-316X2023-01-0111210.1109/TMLCN.2023.326838210116055Inaugural EditorialWalid Saad0https://orcid.org/0000-0003-2247-2458Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA, USAI am delighted to introduce the inaugural volume of the IEEE Transactions on Machine Learning in Communications and Networking (TMLCN), which is an interdisciplinary journal supported by the IEEE Communications Society, the IEEE Signal Processing Society, the IEEE Vehicular Technology Society, and the IEEE Computer Society. The relationship between communication systems and artificial intelligence (AI) is not new, and it has gone through multiple phases over the past couple of decades. Early on, researchers working on communication and networking technologies, such as wireless systems, viewed AI as an unnecessary, and unreliable tool (e.g., compared to the rigor of communication theory and information theory) that could never play any role in the design of communication systems. Subsequently, there was a renewed surge of interest in AI during the incipient stages of so-called cognitive radio networks. At the time, AI appeared to have a potentially important role in the search for dynamic spectrum sharing and access techniques. However, the complexity of AI tools such as neural networks and the shortage of computing resources at the time, meant that those efforts had very limited success. However, the dawn of deep learning architectures, coupled with the recent leap in AI technologies propelled by the advent of large language models (LLMs), has unequivocally transformed the AI landscape, paving the way for AI to become an indispensable framework for designing present and forthcoming communication and networking systems. This is corroborated by several current initiatives across academia, industry, and the government that are looking at what a future “AI-native” wireless system could look like.https://ieeexplore.ieee.org/document/10116055/ |
| spellingShingle | Walid Saad Inaugural Editorial IEEE Transactions on Machine Learning in Communications and Networking |
| title | Inaugural Editorial |
| title_full | Inaugural Editorial |
| title_fullStr | Inaugural Editorial |
| title_full_unstemmed | Inaugural Editorial |
| title_short | Inaugural Editorial |
| title_sort | inaugural editorial |
| url | https://ieeexplore.ieee.org/document/10116055/ |
| work_keys_str_mv | AT walidsaad inauguraleditorial |