MARC details
| 000 -LEADER |
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| fixed length control field |
02207nam a22002777a 4500 |
| 001 - CONTROL NUMBER |
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| control field |
47996 |
| 003 - CONTROL NUMBER IDENTIFIER |
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OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20250520100803.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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250520b |||||||| |||| 00| 0 eng d |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
XU Jiarui |
| Relator term |
Author |
| 245 ## - TITLE STATEMENT |
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| Title |
Learning at the Speed of Wireless: |
| Remainder of title |
Online Real-Time Learning for AI-Enabled MIMO in NextG/ |
| Statement of responsibility, etc. |
Jiarui Xu,Shashank Jere, Yifei Song, Yi-Hung Kao, Lizhong Zheng, Lingjia Liu |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Date of publication, distribution, etc. |
2025 |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Integration of artificial intelligence (AI) and machine learning (ML) into the air interface has been envisioned as a key technology for next-generation (NextG) cellular networks. At the air interface, multiple-input multiple-output (MIMO) and its variants, such as multi-user MIMO (MU-MIMO) and massive/full-dimension MIMO, have been key enablers across successive generations of cellular networks with evolving complexity and design challenges. Initiating active investigation into leveraging AI/ML tools to address these challenges for MIMO becomes a critical step toward an AI-enabled NextG air interface. At the NextG air interface, the underlying wireless environment will be extremely dynamic with operation adaptations performed on a sub-millisecond basis by MIMO operations such as MU-MIMO scheduling and rank/link adaptation. Given the enormously large number of operation adaptation possibilities, we contend that online real-time AI/ML-based approaches constitute a promising paradigm. To this end, we outline the inherent challenges and offer insights into the design of such online real-time AI/ML-based solutions for MIMO operations. An online real-time AI/ML-based method for MIMO-OFDM channel estimation is then presented, serving as a potential roadmap for developing similar techniques across various MIMO operations in NextG. |
| 598 ## - BULLETIN HEADING |
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| Bulletin Heading |
MACHINE LEARNING, NEXTG, AI, SPEED |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
SPEED |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
ARTIFICIAL INTELLIGENCE |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
NEXTG |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
WIRELESS |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Shashank Jere |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Yifei Song |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Yi-Hung Kao |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Lizhong Zheng; |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Lingjia Liu |
| 773 ## - HOST ITEM ENTRY |
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| Related parts |
IEEE Communications Magazine, Volume: 63, Number: 1, Issue: 1, 2025, Page: 92-98 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Journal |
| Suppress in OPAC |
No |
