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The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks, divided into three progressive stages. The first stage, AI for network, focuses on employing AI to augment network performance, optimize efficiency, and enhance user service experiences. The second stage, network for AI, highlights the role of the network in facilitating and buttressing AI operations and presents key enabling technologies. We compare wireless network large models with conventional large language models (LLMs), and identify key design principles and components for building wireless network architectures. In the final stage, AI as a service, it is anticipated that future 6G networks will innately provide AI functions as services, supporting application scenarios like immersive communication and intelligent industrial robots. Specifically, we define the quality of AI service, which refers to a framework for measuring AI services within the network. We further summarize the standardization process of AI for wireless networks, highlighting key milestones and ongoing efforts. In addition, we analyze the critical challenges faced by the integration of AI and communications in 6G. Finally, we outline promising future research opportunities that are expected to drive the development and refinement of AI and 6G communications.<\/jats:p>","DOI":"10.1007\/s11432-024-4337-1","type":"journal-article","created":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T23:17:51Z","timestamp":1743895071000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":126,"title":["Overview of AI and communication for 6G network: fundamentals, challenges, and future research opportunities"],"prefix":"10.1007","volume":"68","author":[{"given":"Qimei","family":"Cui","sequence":"first","affiliation":[]},{"given":"Xiaohu","family":"You","sequence":"additional","affiliation":[]},{"given":"Ni","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Guoshun","family":"Nan","sequence":"additional","affiliation":[]},{"given":"Xuefei","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jianhua","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xinchen","family":"Lyu","sequence":"additional","affiliation":[]},{"given":"Ming","family":"Ai","sequence":"additional","affiliation":[]},{"given":"Xiaofeng","family":"Tao","sequence":"additional","affiliation":[]},{"given":"Zhiyong","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Ping","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Qingqing","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Meixia","family":"Tao","sequence":"additional","affiliation":[]},{"given":"Yongming","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Chongwen","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Guangyi","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Chenghui","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Zhiwen","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Dusit","family":"Niyato","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Muhammad Khurram","family":"Khan","sequence":"additional","affiliation":[]},{"given":"Abbas","family":"Jamalipour","sequence":"additional","affiliation":[]},{"given":"Mohsen","family":"Guizani","sequence":"additional","affiliation":[]},{"given":"Chau","family":"Yuen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,2]]},"reference":[{"key":"4337_CR1","doi-asserted-by":"publisher","first-page":"1617","DOI":"10.1109\/COMST.2016.2532458","volume":"18","author":"M Agiwal","year":"2016","unstructured":"Agiwal M, Roy A, Saxena N. 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