{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T17:44:28Z","timestamp":1778694268978,"version":"3.51.4"},"reference-count":306,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Mathematics of Information Technology and Complex Systems (MITACS) and Ericsson Canada"},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada (NSERC) Canada Research Chairs Program","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Commun. Surv. Tutorials"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/comst.2025.3648785","type":"journal-article","created":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T18:26:09Z","timestamp":1766773569000},"page":"4359-4393","source":"Crossref","is-referenced-by-count":2,"title":["Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey"],"prefix":"10.1109","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2386-3559","authenticated-orcid":false,"given":"Han","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4119-1285","authenticated-orcid":false,"given":"Mohammad","family":"Farzanullah","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7720-3370","authenticated-orcid":false,"given":"Mohammad","family":"Ghassemi","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1260-2853","authenticated-orcid":false,"given":"Akram","family":"Bin Sediq","sequence":"additional","affiliation":[{"name":"Ericsson, Ottawa, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5253-2429","authenticated-orcid":false,"given":"Ali","family":"Afana","sequence":"additional","affiliation":[{"name":"Ericsson, Ottawa, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6787-8457","authenticated-orcid":false,"given":"Melike","family":"Erol-Kantarci","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpat.2023.102754"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s12599-024-00851-0"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-30761-2"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-024-02443-6"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref6","article-title":"Scaling laws for neural language models","author":"Kaplan","year":"2020","journal-title":"arXiv:2001.08361"},{"key":"ref7","article-title":"On the opportunities and risks of foundation models","author":"Bommasani","year":"2021","journal-title":"arXiv:2108.07258"},{"key":"ref8","article-title":"A survey of large language models","author":"Xin Zhao","year":"2023","journal-title":"arXiv:2303.18223"},{"key":"ref9","article-title":"GPT-4 technical report","volume-title":"arXiv:2303.08774","author":"Achiam","year":"2023"},{"issue":"8","key":"ref10","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford","year":"2019","journal-title":"OpenAI Blog"},{"issue":"240","key":"ref11","first-page":"1","article-title":"PaLM: Scaling language modeling with pathways","volume":"24","author":"Chowdhery","year":"2022","journal-title":"J. Mach. Learn. Res."},{"key":"ref12","article-title":"LLaMA: Open and efficient foundation language models","author":"Touvron","year":"2023","journal-title":"arXiv:2302.13971"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2024.3444742"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186150"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1810.04805"},{"key":"ref17","first-page":"8821","article-title":"Zero-shot text-to-image generation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ramesh"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.30574\/msarr.2024.10.1.0028"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/MVT.2019.2919236"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2024.3425594"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/jproc.2025.3526887"},{"key":"ref22","article-title":"6G network business support system","author":"Ouyang","year":"2023","journal-title":"arXiv:2307.10004"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.015.2300404"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2023.3336917"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.006.2200730"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2975004"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/IC2EM59347.2023.10419512"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2024.052618"},{"key":"ref29","first-page":"1","article-title":"An image is worth 16\u00d716 words: Transformers for image recognition at scale","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Dosovitskiy"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2022.3219840"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICC45041.2023.10279112"},{"key":"ref32","article-title":"Recent advances in data-driven intelligent control for wireless communication: A comprehensive survey","author":"Huo","year":"2024","journal-title":"arXiv:2408.02943"},{"issue":"3","key":"ref33","first-page":"1","article-title":"Large language models: A comprehensive survey of its applications, challenges, limitations, and future prospects","volume":"1","author":"Hadi","year":"2024","journal-title":"Authorea Preprints"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1093\/nsr\/nwae403"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3641289"},{"key":"ref36","article-title":"Pushing large language models to the 6G edge: Vision, challenges, and opportunities","author":"Lin","year":"2023","journal-title":"arXiv:2309.16739"},{"key":"ref37","article-title":"Large language models in 6G security: Challenges and opportunities","author":"Nguyen","year":"2024","journal-title":"arXiv:2403.12239"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2024.3465447"},{"key":"ref39","article-title":"A comprehensive survey of AI-generated content (AIGC): A history of generative AI from GAN to ChatGPT","author":"Cao","year":"2023","journal-title":"arXiv:2303.04226"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.58496\/BJAI\/2023\/003"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.3390\/fi15080260"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/s12243-023-00980-9"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2024.3438379"},{"key":"ref44","article-title":"A survey of hallucination in large foundation models","author":"Rawte","year":"2023","journal-title":"arXiv:2309.05922"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-024-10915-y"},{"key":"ref46","article-title":"Telecom foundation models: Applications, challenges, and future trends","author":"Zanouda","year":"2024","journal-title":"arXiv:2408.03964"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2025.3541952"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfds.2017.05.001"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/s12525-021-00475-2"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-016-0043-6"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.3390\/info15080491"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1145\/3611651"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.3390\/jpm12091359"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3460480"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3415112"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1145\/3744746"},{"key":"ref57","article-title":"Language models are general-purpose interfaces","author":"Hao","year":"2022","journal-title":"arXiv:2206.06336"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-024-40231-1"},{"key":"ref59","article-title":"Sparks of artificial general intelligence: Early experiments with GPT-4","author":"Bubeck","year":"2023","journal-title":"arXiv:2303.12712"},{"key":"ref60","first-page":"5098","article-title":"Are emergent abilities in large language models just in-context learning?","volume-title":"Proc. 62nd Annu. Meeting Assoc. Comput. Linguistics","author":"Lu"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1145\/3729218"},{"key":"ref62","article-title":"Emergent abilities of large language models","author":"Wei","year":"2022","journal-title":"arXiv:2206.07682"},{"key":"ref63","first-page":"1877","article-title":"Language models are few-shot learners","volume-title":"Proc. NIPS","author":"Brown"},{"key":"ref64","first-page":"1107","article-title":"A survey on in-context learning","volume-title":"Proc. Conf. Empirical Methods Natural Lang. Process.","author":"Dong"},{"key":"ref65","article-title":"In-context learning unlocked for diffusion models","author":"Wang","year":"2023","journal-title":"arXiv:2305.01115"},{"key":"ref66","article-title":"Many-shot in-context learning in multimodal foundation models","author":"Jiang","year":"2024","journal-title":"arXiv:2405.09798"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1038\/s41562-023-01659-w"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.52202\/079017-3739"},{"issue":"140","key":"ref69","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"ref70","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"139","author":"Radford"},{"key":"ref71","article-title":"Hierarchical text-conditional image generation with CLIP latents","author":"Ramesh","year":"2022","journal-title":"arXiv:2204.06125"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref73","article-title":"Evaluating large language models trained on code","author":"Chen","year":"2021","journal-title":"arXiv:2107.03374"},{"key":"ref74","volume-title":"NVIDIA ISAAC GR00T N1: An Open Foundation Model for Humanoid Robots","author":"Zhu","year":"2025"},{"key":"ref75","volume-title":"BLOOM: A 176B-Parameter Open-Access Multilingual Language Model","author":"Scao","year":"2023"},{"key":"ref76","first-page":"1","article-title":"A generalist agent","volume-title":"Proc. Trans. Mach. Learn. Res.","author":"Reed"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01457"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-92611-2_4"},{"key":"ref79","article-title":"Gemini: A family of highly capable multimodal models","author":"Team","year":"2023","journal-title":"arXiv:2312.11805"},{"key":"ref80","article-title":"DeepSeek-r1: Incentivizing reasoning capability in LLMs via reinforcement learning","author":"Guo","year":"2025","journal-title":"arXiv:2501.12948"},{"key":"ref81","article-title":"Large wireless model (LWM): A foundation model for wireless channels","author":"Alikhani","year":"2024","journal-title":"arXiv:2411.08872"},{"key":"ref82","article-title":"Llama 2: Open foundation and fine-tuned chat models","author":"Touvron","year":"2023","journal-title":"arXiv:2307.09288"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2024.3421306"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLCN59089.2024.10624786"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2023.3321967"},{"key":"ref86","first-page":"973","article-title":"GEMEL: Model merging for memory-efficient, real-time video analytics at the edge","volume-title":"Proc. 20th USENIX Symp. Networked Syst. Design Implement. (NSDI 23)","author":"Padmanabhan"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671465"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00490"},{"key":"ref89","article-title":"On-device language models: A comprehensive review","author":"Xu","year":"2024","journal-title":"arXiv:2409.00088"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.008.2300516"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2007.904944"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2024.3401686"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2023.3322983"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/VTC2020-Spring48590.2020.9129369"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3176025"},{"key":"ref96","article-title":"DINOv2: Learning robust visual features without supervision","author":"Oquab","year":"2023","journal-title":"arXiv:2304.07193"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72970-6_3"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2020.3003670"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1109\/GCWkshps56602.2022.10008648"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3104219"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2024.3392799"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.007.2200155"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1186\/s13638-020-01829-8"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.006.2400131"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.2200810"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1109\/OJCOMS.2021.3128637"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2025.3527561"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380027"},{"key":"ref110","article-title":"Towards graph foundation models: A survey and beyond","author":"Liu","year":"2023","journal-title":"arXiv:2310.11829"},{"key":"ref111","first-page":"1","article-title":"Position: Graph foundation models are already here","volume-title":"Proc. 41st Int. Conf. Mach. Learn.","author":"Mao"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1145\/3625687.3625793"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615155"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539321"},{"key":"ref115","article-title":"GraphFM: A comprehensive benchmark for graph foundation model","author":"Xu","year":"2024","journal-title":"arXiv:2406.08310"},{"key":"ref116","article-title":"GraphFM: A scalable framework for multi-graph pretraining","author":"Lachi","year":"2024","journal-title":"arXiv:2407.11907"},{"key":"ref117","doi-asserted-by":"publisher","DOI":"10.1109\/GCWkshps45667.2019.9024538"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM46510.2021.9685457"},{"key":"ref119","doi-asserted-by":"publisher","DOI":"10.1109\/NOMS54207.2022.9789878"},{"issue":"19","key":"ref120","first-page":"6692","article-title":"A graph neural network method for distributed anomaly detection in IoT","volume":"21","author":"Protogerou","year":"2021","journal-title":"Sensors"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-10826-0"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3005434"},{"key":"ref123","article-title":"Point-SAM: Promptable 3D segmentation model for point clouds","author":"Zhou","year":"2024","journal-title":"arXiv:2406.17741"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72698-9_8"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01871"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.1109\/JCS64661.2025.10880635"},{"key":"ref127","article-title":"Millimeter wave wireless communication assisted three-dimensional simultaneous localization and mapping","author":"Mou","year":"2023","journal-title":"arXiv:2303.02617"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1109\/JMMCT.2024.3464373"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2023.3282594"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i9.33001"},{"key":"ref131","article-title":"NetOrchLLM: Mastering wireless network orchestration with large language models","author":"Abdallah","year":"2024","journal-title":"arXiv:2412.10107"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2022.3219409"},{"key":"ref133","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2025.3529082"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.004.2400281"},{"key":"ref135","doi-asserted-by":"publisher","DOI":"10.1109\/IPIN62893.2024.10786154"},{"key":"ref136","article-title":"TimeGPT-1","author":"Garza","year":"2023","journal-title":"arXiv:2310.03589"},{"key":"ref137","first-page":"1","article-title":"TimeDiT: General-purpose diffusion transformers for time series foundation model","volume-title":"Proc. ICML Workshop Found. Models Wild","author":"Cao"},{"key":"ref138","first-page":"1","article-title":"Lag-Llama: Towards foundation models for time series forecasting","volume-title":"Proc. Robustness Few-Shot Zero-Shot Learn. Large Found. Models","author":"Rasul"},{"key":"ref139","first-page":"1","article-title":"Monash time series forecasting archive","volume-title":"Proc. 35th Conf. Neural Inf. Process. Syst. Datasets Benchmarks Track","author":"Godahewa"},{"key":"ref140","first-page":"1","article-title":"A decoder-only foundation model for time-series forecasting","volume-title":"Proc. 41st Int. Conf. Mach. Learn.","author":"Das"},{"key":"ref141","first-page":"16061","article-title":"MOMENT: A family of open time-series foundation models","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Goswami"},{"key":"ref142","doi-asserted-by":"publisher","DOI":"10.1145\/3719207"},{"key":"ref143","article-title":"One fits all: Power general time series analysis by pretrained LM","author":"Zhou","year":"2023","journal-title":"arXiv:2302.11939"},{"key":"ref144","article-title":"TEMPO: Prompt-based generative pre-trained transformer for time series forecasting","author":"Cao","year":"2023","journal-title":"arXiv:2310.04948"},{"key":"ref145","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i21.30383"},{"key":"ref146","article-title":"Lag-Llama: Towards foundation models for probabilistic time series forecasting","author":"Rasul","year":"2023","journal-title":"arXiv:2310.08278"},{"key":"ref147","article-title":"Understanding the role of textual prompts in llm for time series forecasting: an adapter view","author":"Niu","year":"2023","journal-title":"arXiv:2311.14782"},{"key":"ref148","article-title":"Lens: A foundation model for network traffic","author":"Wang","year":"2024","journal-title":"arXiv:2402.03646"},{"key":"ref149","article-title":"FOMO: A foundation model for mobile traffic forecasting with diffusion model","author":"Chai","year":"2024","journal-title":"arXiv:2410.15322"},{"key":"ref150","doi-asserted-by":"publisher","DOI":"10.1145\/3773912"},{"key":"ref151","doi-asserted-by":"publisher","DOI":"10.1109\/ICCWorkshops59551.2024.10615438"},{"key":"ref152","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2023.3270441"},{"key":"ref153","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2021.3136707"},{"key":"ref154","doi-asserted-by":"publisher","DOI":"10.1016\/j.commtr.2024.100150"},{"key":"ref155","article-title":"Self-refined generative foundation models for wireless traffic prediction","author":"Hu","year":"2024","journal-title":"arXiv:2408.10390"},{"key":"ref156","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2018.2848960"},{"key":"ref157","doi-asserted-by":"publisher","DOI":"10.23919\/JCIN.2024.10582829"},{"key":"ref158","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-025-4349-0"},{"key":"ref159","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.2400780"},{"key":"ref160","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM46510.2021.9685587"},{"key":"ref161","article-title":"A generalized transformer-based radio link failure prediction framework in 5G RANs","author":"Hasan","year":"2024","journal-title":"arXiv:2407.05197"},{"key":"ref162","doi-asserted-by":"publisher","DOI":"10.1109\/ICC52391.2025.11161462"},{"key":"ref163","article-title":"WirelessAgent: Large language model agents for intelligent wireless networks","author":"Tong","year":"2024","journal-title":"arXiv:2409.07964"},{"key":"ref164","article-title":"Agentic AI: Autonomy, accountability, and the algorithmic society","author":"Mukherjee","year":"2025","journal-title":"arXiv:2502.00289"},{"key":"ref165","first-page":"1","article-title":"LiFT: Unsupervised reinforcement learning with foundation models as teachers","volume-title":"Proc. 2nd Agent Learn. Open-Endedness Workshop","author":"Nam"},{"key":"ref166","doi-asserted-by":"publisher","DOI":"10.1177\/02783649241281508"},{"key":"ref167","article-title":"LLM-based intent processing and network optimization using attention-based hierarchical reinforcement learning","author":"Habib","year":"2024","journal-title":"arXiv:2406.06059"},{"key":"ref168","doi-asserted-by":"publisher","DOI":"10.34133\/icomputing.0063"},{"key":"ref169","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.016.2300600"},{"key":"ref170","doi-asserted-by":"publisher","DOI":"10.1109\/GCWkshp64532.2024.11101226"},{"key":"ref171","article-title":"Generative AI-in-the-loop: Integrating LLMs and GPTs into the next generation networks","author":"Zhang","year":"2024","journal-title":"arXiv:2406.04276"},{"key":"ref172","first-page":"1","article-title":"Reinforcement learning with foundation priors: Let embodied agent efficiently learn on its own","volume-title":"Proc. 8th Annu. Conf. Robot Learn.","author":"Ye"},{"key":"ref173","doi-asserted-by":"publisher","DOI":"10.1109\/NetSoft60951.2024.10588921"},{"key":"ref174","article-title":"An overview of machine learning-enabled optimization for reconfigurable intelligent surfaces-aided 6G networks: From reinforcement learning to large language models","author":"Zhou","year":"2024","journal-title":"arXiv:2405.17439"},{"key":"ref175","first-page":"2463","article-title":"Language models as knowledge bases?","volume-title":"Proc. Conf. Empirical Methods Natural Lang. Process. 9th Int. Joint Conf. Natural Lang. Process. (EMNLP-IJCNLP)","author":"Petroni"},{"key":"ref176","doi-asserted-by":"publisher","DOI":"10.1109\/LNET.2024.3486260"},{"key":"ref177","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2024.3493463"},{"key":"ref178","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3113051"},{"key":"ref179","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2866979"},{"key":"ref180","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2023.3348203"},{"key":"ref181","first-page":"1","article-title":"Contrastive behavioral similarity embeddings for generalization in reinforcement learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Agarwal"},{"key":"ref182","article-title":"Foundation models for decision making: Problems, methods, and opportunities","author":"Yang","year":"2023","journal-title":"arXiv:2303.04129"},{"key":"ref183","article-title":"Representation learning with contrastive predictive coding","author":"Van Den Oord","year":"2018","journal-title":"arXiv:1807.03748"},{"key":"ref184","doi-asserted-by":"publisher","DOI":"10.1109\/PIMRC62392.2025.11274561"},{"key":"ref185","article-title":"Agent AI: Surveying the horizons of multimodal interaction","author":"Durante","year":"2024","journal-title":"arXiv:2401.03568"},{"key":"ref186","first-page":"1","article-title":"Text2Reward: Reward shaping with language models for reinforcement learning","volume-title":"Proc. 12th Int. Conf. Learn. Represent.","author":"Xie"},{"key":"ref187","doi-asserted-by":"publisher","DOI":"10.15866\/irecap.v7i1.10475"},{"key":"ref188","first-page":"31199","article-title":"Pre-trained language models for interactive decision-making","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Li"},{"key":"ref189","first-page":"1","article-title":"Towards a unified agent with foundation models","volume-title":"Proc. Workshop Reincarnating Reinforcement Learn. ICLR","author":"Palo"},{"key":"ref190","article-title":"Voyager: An open-ended embodied agent with large language models","author":"Wang","year":"2023","journal-title":"arXiv:2305.16291"},{"key":"ref191","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2024.3507801"},{"key":"ref192","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.005.2400019"},{"key":"ref193","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2024.3422309"},{"key":"ref194","article-title":"ALYMPICS: Language agents meet game theory","author":"Mao","year":"2023","journal-title":"arXiv:2311.03220"},{"key":"ref195","article-title":"Game-theoretic LLM: Agent workflow for negotiation games","author":"Hua","year":"2024","journal-title":"arXiv:2411.05990"},{"key":"ref196","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.007.2400133"},{"key":"ref197","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.007.2400124"},{"key":"ref198","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/808"},{"key":"ref199","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2015.55"},{"key":"ref200","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2015.7248881"},{"key":"ref201","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-33778-0_11"},{"key":"ref202","doi-asserted-by":"publisher","DOI":"10.1145\/3204949.3208123"},{"key":"ref203","doi-asserted-by":"publisher","DOI":"10.1145\/3339825.3394938"},{"key":"ref204","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.108736"},{"key":"ref205","doi-asserted-by":"publisher","DOI":"10.1109\/LNET.2021.3098455"},{"key":"ref206","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.2987994"},{"key":"ref207","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.2200679"},{"key":"ref208","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2020.3014049"},{"key":"ref209","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3082898"},{"key":"ref210","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482152"},{"key":"ref211","article-title":"6g-path_wp1_dataset_uc-cities-1_experimental_ran_traffic_it","author":"De Telecomunica\u00e7\u00f5es","year":"2025"},{"key":"ref212","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOMWKSHPS57453.2023.10225784"},{"issue":"9","key":"ref213","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.cja.2021.08.016","article-title":"Large-scale real-world radio signal recognition with deep learning","volume":"35","author":"Ya","year":"2022","journal-title":"Chin. J. Aeronaut."},{"key":"ref214","volume-title":"The Radio Frequency Spectrum + Machine Learning = A New Wave in Radio Technology","year":"2017"},{"key":"ref215","doi-asserted-by":"publisher","DOI":"10.1109\/IOTM.0001.1900065"},{"key":"ref216","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2018.2797022"},{"key":"ref217","volume-title":"PS-002 WALDO","year":"2025"},{"key":"ref218","doi-asserted-by":"publisher","DOI":"10.1109\/WCNC51071.2022.9771569"},{"key":"ref219","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.3027636"},{"key":"ref220","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2019.2899571"},{"key":"ref221","article-title":"ViWi vision-aided mmWave beam tracking: Dataset, task, and baseline solutions","author":"Alrabeiah","year":"2020","journal-title":"arXiv:2002.02445"},{"key":"ref222","doi-asserted-by":"publisher","DOI":"10.1109\/VTC2021-Fall52928.2021.9625195"},{"key":"ref223","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2024.3351748"},{"key":"ref224","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2022.3170733"},{"key":"ref225","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM48880.2022.9796865"},{"key":"ref226","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP49357.2023.10096315"},{"key":"ref227","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.002.2200028"},{"key":"ref228","article-title":"Multi-modal beam prediction challenge 2022: Towards generalization","author":"Charan","year":"2022","journal-title":"arXiv:2209.07519"},{"key":"ref229","doi-asserted-by":"publisher","DOI":"10.1109\/GCWkshps56602.2022.10008524"},{"key":"ref230","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM52923.2024.10901692"},{"key":"ref231","doi-asserted-by":"publisher","DOI":"10.23919\/JCC.fa.2023-0268.202311"},{"key":"ref232","doi-asserted-by":"publisher","DOI":"10.1109\/ICCC62479.2024.10682002"},{"key":"ref233","doi-asserted-by":"publisher","DOI":"10.1109\/lwc.2025.3558059"},{"key":"ref234","doi-asserted-by":"publisher","DOI":"10.1109\/tvt.2025.3555130"},{"key":"ref235","doi-asserted-by":"publisher","DOI":"10.1109\/mcom.002.2400687"},{"key":"ref236","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2024.3408041"},{"key":"ref237","article-title":"Sensiverse: A dataset for ISAC study","author":"Luo","year":"2023","journal-title":"arXiv:2308.13789"},{"key":"ref238","doi-asserted-by":"publisher","DOI":"10.1109\/Ucom62433.2024.10695938"},{"key":"ref239","doi-asserted-by":"publisher","DOI":"10.1109\/PIMRC62392.2025.11275520"},{"key":"ref240","doi-asserted-by":"publisher","DOI":"10.1109\/ICCWorkshops67674.2025.11162434"},{"key":"ref241","article-title":"IQFM a wireless foundational model for I\/Q streams in AI-native 6G","author":"Mashaal","year":"2025","journal-title":"arXiv:2506.06718"},{"key":"ref242","article-title":"Transformer models: An introduction and catalog","author":"Amatriain","year":"2023","journal-title":"arXiv:2302.07730"},{"key":"ref243","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2024.3379244"},{"key":"ref244","doi-asserted-by":"publisher","DOI":"10.1145\/3626235"},{"key":"ref245","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00387"},{"key":"ref246","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-emnlp.936"},{"key":"ref247","first-page":"1","article-title":"Mamba: Linear-time sequence modeling with selective state spaces","volume-title":"Proc. 1st Conf. Lang. Model.","author":"Gu"},{"key":"ref248","first-page":"5156","article-title":"Transformers are RNNs: Fast autoregressive transformers with linear attention","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"1","author":"Katharopoulos"},{"key":"ref249","article-title":"Rethinking attention with performers","author":"Choromanski","year":"2020","journal-title":"arXiv:2009.14794"},{"key":"ref250","first-page":"16344","article-title":"FlashAttention: Fast and memory-efficient exact attention with IO-awareness","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Dao"},{"key":"ref251","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00009"},{"key":"ref252","article-title":"Root cause analysis of anomalies in 5G RAN using graph neural network and transformer","author":"Hasan","year":"2024","journal-title":"arXiv:2406.15638"},{"key":"ref253","article-title":"Between words and characters: A brief history of open-vocabulary modeling and tokenization in NLP","author":"Mielke","year":"2021","journal-title":"arXiv:2112.10508"},{"key":"ref254","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3231442"},{"key":"ref255","first-page":"155","article-title":"Efficient domain adaptation of language models via adaptive tokenization","volume-title":"Proc. 2nd Workshop Simple Efficient Natural Lang. Process.","author":"Sachidananda"},{"key":"ref256","doi-asserted-by":"publisher","DOI":"10.3390\/technologies9010002"},{"key":"ref257","first-page":"9912","article-title":"Unsupervised learning of visual features by contrasting cluster assignments","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Caron"},{"key":"ref258","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525087"},{"key":"ref259","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3194732"},{"key":"ref260","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-12939-2_17"},{"key":"ref261","article-title":"A multi-task foundation model for wireless channel representation using contrastive and masked autoencoder learning","author":"Guler","year":"2025","journal-title":"arXiv:2505.09160"},{"key":"ref262","article-title":"Learn from model beyond fine-tuning: A survey","author":"Zheng","year":"2023","journal-title":"arXiv:2310.08184"},{"key":"ref263","volume-title":"Improving Language Understanding by Generative Pre-Training","author":"Radford","year":"2018"},{"key":"ref264","first-page":"2790","article-title":"Parameter-efficient transfer learning for NLP","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Houlsby"},{"key":"ref265","article-title":"Towards a unified view of parameter-efficient transfer learning","author":"He","year":"2021","journal-title":"arXiv:2110.04366"},{"key":"ref266","article-title":"Less is more: Selective layer finetuning with SubTuning","author":"Kaplun","year":"2023","journal-title":"arXiv:2302.06354"},{"key":"ref267","first-page":"1","article-title":"LoRA: Low-rank adaptation of large language models","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Hu"},{"key":"ref268","doi-asserted-by":"publisher","DOI":"10.1145\/3678957.3685724"},{"key":"ref269","article-title":"UniPELT: A unified framework for parameter-efficient language model tuning","author":"Mao","year":"2021","journal-title":"arXiv:2110.07577"},{"key":"ref270","article-title":"LLM-adapters: An adapter-based framework for parameter-efficient fine-tuning of large language models","author":"Hu","year":"2022","journal-title":"arXiv:2204.08773"},{"key":"ref271","article-title":"Parameter-efficient fine-tuning design spaces","volume-title":"Proc. 11th Int. Conf. Learn. Represent.","author":"Chen"},{"key":"ref272","doi-asserted-by":"publisher","DOI":"10.1145\/3240765.3243479"},{"issue":"6","key":"ref273","first-page":"1","article-title":"A survey of quantization methods for efficient deep neural network inference","volume":"54","author":"Gholami","year":"2021","journal-title":"ACM Comput. Surveys"},{"key":"ref274","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00286"},{"key":"ref275","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-024-04686-y"},{"key":"ref276","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"ref277","article-title":"Fast model editing at scale","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Mitchell"},{"key":"ref278","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3524255"},{"key":"ref279","volume-title":"Llama.cpp: LLM Inference in Pure C\/C++","year":"2023"},{"key":"ref280","article-title":"Prima. Cpp: Speeding up 70b-scale LLM inference on low-resource everyday home clusters","author":"Li","year":"2025","journal-title":"arXiv:2504.08791"},{"key":"ref281","volume-title":"MLC-LLM","year":"2025"},{"key":"ref282","volume-title":"AirLLM: Scaling Large Language Models on Low-End Commodity Computers","author":"Li","year":"2023"},{"key":"ref283","volume-title":"EXO: Run Your Own AI Cluster at Home With Everyday Devices","year":"2024"},{"key":"ref284","volume-title":"Distributed Llama","author":"Tadych","year":"2024"},{"key":"ref285","doi-asserted-by":"publisher","DOI":"10.1109\/ICC52391.2025.11161391"},{"key":"ref286","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM52122.2024.10621342"},{"key":"ref287","article-title":"LinguaLinked: A distributed large language model inference system for mobile devices","author":"Zhao","year":"2023","journal-title":"arXiv:2312.00388"},{"key":"ref288","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2025.3596892"},{"key":"ref289","doi-asserted-by":"publisher","DOI":"10.1145\/3650200.3656628"},{"key":"ref290","article-title":"When foundation model meets federated learning: Motivations, challenges, and future directions","author":"Zhuang","year":"2023","journal-title":"arXiv:2306.15546"},{"key":"ref291","doi-asserted-by":"publisher","DOI":"10.1109\/tbdata.2024.3524105"},{"key":"ref292","article-title":"FedDAT: Federated dual-adapter teacher for parameter-efficient fine-tuning","author":"He","year":"2023","journal-title":"arXiv:2310.03818"},{"key":"ref293","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2023.3302410"},{"key":"ref294","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.005.2300481"},{"key":"ref295","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2025.3531128"},{"key":"ref296","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2024.3427313"},{"key":"ref297","article-title":"Prompt engineering or fine tuning: An empirical assessment of large language models in automated software engineering tasks","author":"Shin","year":"2023","journal-title":"arXiv:2310.10508"},{"key":"ref298","article-title":"Hierarchical federated foundation models over wireless networks for multi-modal multi-task intelligence: Integration of edge learning with D2D\/P2P-enabled fog learning architectures","author":"Abdisarabshali","year":"2025","journal-title":"arXiv:2509.03695"},{"key":"ref299","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-025-11389-2"},{"key":"ref300","volume-title":"Democratizing On-Device Generative AI With Sub-10 Billion Parameter Models","year":"2023"},{"key":"ref301","article-title":"Transformer-lite: High-efficiency deployment of large language models on mobile phone GPUs","author":"Li","year":"2024","journal-title":"arXiv:2403.20041"},{"key":"ref302","doi-asserted-by":"publisher","DOI":"10.37256\/cnc.3220256807"},{"key":"ref303","article-title":"Solving AI foundational model latency with Telco infrastructure","author":"Barros","year":"2025","journal-title":"arXiv:2504.03708"},{"key":"ref304","doi-asserted-by":"publisher","DOI":"10.1109\/OJCOMS.2024.3362271"},{"key":"ref305","first-page":"1","article-title":"ReAct: Synergizing reasoning and acting in language models","volume-title":"Proc. 11th Int. Conf. Learn. Represent.","author":"Yao"},{"key":"ref306","first-page":"68539","article-title":"Toolformer: Language models can teach themselves to use tools","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Schick"}],"container-title":["IEEE Communications Surveys &amp; Tutorials"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/9739\/11321210\/11316403.pdf?arnumber=11316403","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T20:53:29Z","timestamp":1768510409000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11316403\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":306,"URL":"https:\/\/doi.org\/10.1109\/comst.2025.3648785","relation":{},"ISSN":["1553-877X","2373-745X"],"issn-type":[{"value":"1553-877X","type":"electronic"},{"value":"2373-745X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}