{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T09:25:53Z","timestamp":1773998753947,"version":"3.50.1"},"reference-count":20,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,8]]},"DOI":"10.1109\/globecom59602.2025.11431647","type":"proceedings-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T20:04:01Z","timestamp":1773950641000},"page":"841-846","source":"Crossref","is-referenced-by-count":0,"title":["Functional Data Analysis-Guided Prompt Design for RFID Sensing and Localization Using LLMs"],"prefix":"10.1109","author":[{"given":"Yujie","family":"Sun","sequence":"first","affiliation":[{"name":"Auburn University,Department of Electrical and Computer Engineering,Auburn,AL,36849-5201"}]},{"given":"Xuyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Florida International University,Knight Foundation School of Computing and Information Sciences,Miami,FL,33199"}]},{"given":"Guanqun","family":"Cao","sequence":"additional","affiliation":[{"name":"Michigan State University,Department of Statistics &#x0026; Probability,East Lansing,MI,48824"}]},{"given":"Shiwen","family":"Mao","sequence":"additional","affiliation":[{"name":"Auburn University,Department of Electrical and Computer Engineering,Auburn,AL,36849-5201"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s12544-015-0170-8"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICACCI.2017.8126198"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/comst.2025.3526606"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/comst.2024.3503680"},{"key":"ref5","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/2024.findings-emnlp.201","article-title":"Language models still struggle to zero-shot reason about time series","author":"Merrill","year":"2024"},{"key":"ref6","article-title":"LLM4TS: Aligning pre-trained LLMs as data-efficient time-series forecasters","author":"Chang","year":"2023"},{"key":"ref7","article-title":"Tiny time mixers (TTMs): Fast pre-trained models for enhanced zero\/few-shot forecasting of multivariate time series","author":"Ekambaram","year":"2024"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICDEW61823.2024.00022"},{"key":"ref9","article-title":"UniTime: A language-empowered unified model for cross-domain time series forecasting","author":"Liu","year":"2023"},{"key":"ref10","article-title":"Wav2Prompt: End-to-end speech prompt generation and tuning for LLM in zero and few-shot learning","author":"Deng","year":"2024"},{"key":"ref11","article-title":"Large language models are zero-shot time series forecasters","author":"Gruver","year":"2024"},{"key":"ref12","article-title":"GPT-4 Technical Report","year":"2024"},{"key":"ref13","article-title":"The first step is the hardest: Pitfalls of representing and tokenizing temporal data for Large Language Models","author":"Spathis","year":"2023"},{"key":"ref14","article-title":"Time-LLM: Time series forecasting by reprogramming Large Language Models","author":"Jin","year":"2024"},{"key":"ref15","article-title":"PromptCast: A new prompt-based learning paradigm for time series forecasting","author":"Xue","year":"2023"},{"key":"ref16","article-title":"TEMPO: Prompt-based generative pre-trained transformer for time series forecasting","author":"Cao","year":"2023"},{"key":"ref17","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/2024.findings-acl.466","article-title":"LSTPrompt: Large language models as zero-shot time series forecasters by long-short-term prompting","author":"Liu","year":"2024"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3643543"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3351271"},{"key":"ref20","article-title":"Gemini: A family of highly capable multi-modal models","year":"2023"}],"event":{"name":"GLOBECOM 2025 - 2025 IEEE Global Communications Conference","location":"Taipei, Taiwan","start":{"date-parts":[[2025,12,8]]},"end":{"date-parts":[[2025,12,12]]}},"container-title":["GLOBECOM 2025 - 2025 IEEE Global Communications Conference"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11431620\/11431622\/11431647.pdf?arnumber=11431647","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T05:46:26Z","timestamp":1773985586000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11431647\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,8]]},"references-count":20,"URL":"https:\/\/doi.org\/10.1109\/globecom59602.2025.11431647","relation":{},"subject":[],"published":{"date-parts":[[2025,12,8]]}}}