Clustering-NN-Based CFO Estimation Using Random Access Preambles for 5G Non-Terrestrial Networks

Li Zhen, Luyao Cheng, Zheng Chu, Keping Yu, Pei Xiao, Mohsen Guizani

Research output: Journal PublicationArticlepeer-review


Non-terrestrial networks (NTNs) are expected to play a pivotal role in the future wireless ecosystem. Due to its high-dynamic characteristics, the accurate estimation and compensation of carrier frequency offset (CFO) are crucial for supporting 5G new radio (NR) enabled satellite direct access. With emphasis on ensuring reliable uplink synchronization, we propose a clustering-neural network based CFO estimation scheme by virtue of NR random access preambles. By leveraging the sparsity and regularity of input samples, the proposed scheme can achieve fast and precise prediction of CFOs, while establishing robustness against time uncertainty and channel variation within a satellite beam. Simulation results validate the feasibility of our scheme in various NTN scenarios, and demonstrate its superiority in terms of stable estimation performance over the existing schemes.

Original languageEnglish
Pages (from-to)587-591
Number of pages5
JournalIEEE Wireless Communications Letters
Issue number3
Publication statusPublished - 1 Mar 2024


  • Non-terrestrial networks
  • carrier frequency offset estimation
  • clustering
  • neural network
  • random access preamble

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering


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