[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81871-en":3,"doc-seo-81871-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},81871,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Diffusion-Based Noise-Adaptive Null-Space Channel Estimation for OFDM Systems","Accurate channel estimation in orthogonal frequency division multiplexing (OFDM) systems is hindered by sparse and noisy demodulation reference signal (DMRS) observations and by DMRS configurations that change across deployment scenarios. The paper introduces DANCE (Diffusion-based Noise-Adaptive Null-space Channel Estimation), casting DMRS-aided estimation as a sparse linear inverse problem. Range–null space decomposition enforces measurement constraints while reconstructing unobserved components via a learned diffusion prior. A noise-adaptive posterior correction calibrates reverse diffusion to observation noise, reducing pilot-noise injection without losing measurement fidelity, supported by conditional complex-valued U-Net denoising and extensive simulations.","Diffusion-Based Noise-Adaptive Null-Space Channel Estimation for  \nOFDM Systems  \nHeqiang Qi, Yirun Chen, Xiangming Meng†, Chunxiao Jiang, Fellow, IEEE,  \nSheng Wu, Member, IEEE, and Linling Kuang, Member, IEEE  \narXiv :2607 .03348v 1 [ cs .IT] 3 Jul 2026  \nAbstract—Accurate channel estimation in orthogonal frequency division multiplexing (OFDM) systems remains challenging when demodulation reference signal (DMRS) observations are sparse and noisy, and when DMRS configurations vary across deployment scenarios. This paper proposes DANCE (Diffusion-based Noise-Adaptive Null-space Channel Estimation), a diffusion-based channel estimator for OFDM systems. We formulate DMRS-aided channel estimation as a sparse linear inverse problem whose measurement operator is induced by the pilot pattern. The resulting range–null space decomposition separates the measurement-constrained range-space component from the unobserved null-space component, which is reconstructed through a learned diffusion prior. To avoid directly imposing noisy pilot samples as exact constraints, DANCE introducesa noise-adaptive posterior correction into the reverse diffusion process. The correction coefficient and the residual sampling variance are jointly calibrated according to the observation noise level, thereby reducing pilot-noise injection while retaining useful measurement information. We further design a conditional U-Net denoiser for complex-valued OFDM channel grids, where the real and imaginary components are represented as separate feature channels and downsampling is performed only along the subcarrier dimension. Simulations based on 5G NR tapped delay line (TDL) and clustered delay line (CDL) channel models show that DANCE achieves consistently lower normalized mean squared error (NMSE) than conventional estimators and diffusion-based posterior sampling methods under different signal-to-noise ratios, DMRS configurations, Doppler frequency shifts, and train–test distribution mismatches.  \nIndex Terms—Channel estimation, OFDM, generative models, diffusion models  \nI. INTRODUCTION  \nWITH the continued deployment of fifth-generation (5G)  \nnetworks and the development of sixth-generation (6G) wireless systems, radio links are expected to support high data rates, reliable connectivity, and user mobility in increasingly diverse propagation environments [1]–[3] . Orthogonal frequency division multiplexing (OFDM) remains a  \nHeqiang Qi is with the College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China (e-mail: [heqiangqi@zju.edu.cn](heqiangqi@zju.edu.cn)).  \nYirun Chen and Xiangming Meng are with Zhejiang University-University of Illinois Urbana–Champaign Institute, Zhejiang University, Haining 314400, China (e-mail: [yirun.25@intl.zju.edu.cn](yirun.25@intl.zju.edu.cn); [xiangmingmeng@intl.zju.edu.cn](xiangmingmeng@intl.zju.edu.cn)).  \nChunxiao Jiang and Linling Kuang are with the Beijing National Research Center for Information Science and Technology, and the State Key Laboratory of Space Network and Communications, Tsinghua University, Beijing 100084, China (e-mail: [jchx@tsinghua.edu.cn](jchx@tsinghua.edu.cn); [kll@tsinghua.edu.cn](kll@tsinghua.edu.cn)).  \nSheng Wu is with the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China (e-mail: [thuraya@bupt.edu.cn](thuraya@bupt.edu.cn)).  \n†Corresponding author: Xiangming Meng.  \ncentral multicarrier technique because it converts a frequencyselective channel into parallel narrowband subchannels and enables efficient equalization [4] . At the receiver, coherent demodulation and data detection depend on accurate channel state information (CSI) over the time–frequency resource grid. Reliable channel estimation is therefore central to OFDM receiver design and has a direct impact on link reliability and throughput.  \nIn practical OFDM systems, CSI is commonly inferred from demodulation reference si","cbCaimMG7UpY4ZA6","https://ap.wps.com/l/cbCaimMG7UpY4ZA6","pdf",1415789,5,1,13,"English","en",105,"# Abstract\n# Introduction\n## Motivation and problem setup\n## Classical estimators and their limitations\n## Learning-based approaches","[{\"question\":\"What challenge does DANCE address in OFDM channel estimation?\",\"answer\":\"DANCE targets inaccurate estimation caused by sparse, noisy DMRS observations and changing DMRS configurations across scenarios.\"},{\"question\":\"How does DANCE reformulate DMRS-aided channel estimation?\",\"answer\":\"It formulates the task as a sparse linear inverse problem where the measurement operator is induced by the pilot pattern, then uses range–null space decomposition to separate observed constraints from unobserved components.\"},{\"question\":\"How does DANCE reduce the impact of noisy pilot samples?\",\"answer\":\"DANCE introduces a noise-adaptive posterior correction in the reverse diffusion process, jointly calibrating a correction coefficient and residual sampling variance using the observation noise level.\"}]","Diffusion-Based Noise-Adaptive Null-Space Channel Estimation for OFDM Systems | 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challenge does DANCE address in OFDM channel estimation?","Question",{"text":77,"@type":78},"DANCE targets inaccurate estimation caused by sparse, noisy DMRS observations and changing DMRS configurations across scenarios.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does DANCE reformulate DMRS-aided channel estimation?",{"text":82,"@type":78},"It formulates the task as a sparse linear inverse problem where the measurement operator is induced by the pilot pattern, then uses range–null space decomposition to separate observed constraints from unobserved components.",{"name":84,"@type":75,"acceptedAnswer":85},"How does DANCE reduce the impact of noisy pilot samples?",{"text":86,"@type":78},"DANCE introduces a noise-adaptive posterior correction in the reverse diffusion process, jointly calibrating a correction coefficient and residual sampling variance using the observation noise 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