{"title":"A Low-Complexity Chinese Remainder Theorem Based Multi-Carrier Delay Estimation Approach","authors":"Yuxiao Zhang;Shuai Wang;Xuanhe Yang;Jiahao Zhang;Gaofeng Pan;Jianping An;Dusit Niyato","doi":"10.1109/TSP.2025.3557851","DOIUrl":null,"url":null,"abstract":"With the rapid development of cluster systems, such as unmanned aerial vehicle (UAV) networks and satellite constellations, multi-node collaboration has emerged as a critical requirement. Accurate time synchronization, a cornerstone of such collaborative systems, heavily relies on high-precision delay estimation. Traditional multi-carrier delay estimation methods face an inherent trade-off between estimation accuracy and unambiguous range. While the Chinese Remainder Theorem (CRT)-based approach resolves this dilemma by enabling high-precision estimation without sacrificing range, its computational complexity remains prohibitively high for practical implementations. To address this challenge, we propose a novel low-complexity CRT algorithm based on remainder reconstruction (RR-CRT). By introducing an auxiliary phase to reconstruct erroneous remainders, our method reduces the computational complexity from <inline-formula><tex-math>$O(K^{2})$</tex-math></inline-formula> to <inline-formula><tex-math>$O(K)$</tex-math></inline-formula>, where K denotes the number of subcarriers. Crucially, this reduction in complexity only marginally impacts the algorithm's performance, including the phase error tolerance range, the probability of correctly solving phase ambiguity, and the root mean square error (RMSE) of delay estimation. Numerical simulations validate the effectiveness and robustness of the proposed algorithm, demonstrating its superiority in balancing computational efficiency and estimation performance.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1643-1657"},"PeriodicalIF":4.6000,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10949834/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
With the rapid development of cluster systems, such as unmanned aerial vehicle (UAV) networks and satellite constellations, multi-node collaboration has emerged as a critical requirement. Accurate time synchronization, a cornerstone of such collaborative systems, heavily relies on high-precision delay estimation. Traditional multi-carrier delay estimation methods face an inherent trade-off between estimation accuracy and unambiguous range. While the Chinese Remainder Theorem (CRT)-based approach resolves this dilemma by enabling high-precision estimation without sacrificing range, its computational complexity remains prohibitively high for practical implementations. To address this challenge, we propose a novel low-complexity CRT algorithm based on remainder reconstruction (RR-CRT). By introducing an auxiliary phase to reconstruct erroneous remainders, our method reduces the computational complexity from $O(K^{2})$ to $O(K)$, where K denotes the number of subcarriers. Crucially, this reduction in complexity only marginally impacts the algorithm's performance, including the phase error tolerance range, the probability of correctly solving phase ambiguity, and the root mean square error (RMSE) of delay estimation. Numerical simulations validate the effectiveness and robustness of the proposed algorithm, demonstrating its superiority in balancing computational efficiency and estimation performance.
期刊介绍:
The IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals. The term “signal” includes, among others, audio, video, speech, image, communication, geophysical, sonar, radar, medical and musical signals. Examples of topics of interest include, but are not limited to, information processing and the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals.