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Frequency Offset Estimation for OTFS Systems Based on Deep Belief Networks

  • Yuchen Zhang
  • , Rao Fu
  • , Yufei Wang
  • , Xiaohui Dong
  • , Xinxin Yang
  • , Michel Kadoch
  • Beihang University
  • École de technologie supérieure

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

In this study, we introduce a Carrier Frequency Offset (CFO) estimation technique based on Deep Belief Networks (DBN) in the context of deep learning principles. We leverage unsupervised learning methods to pre-train the layers of a Restricted Boltzmann Machine (RBM), keeping the initial weights and biases within an ideal range. Subsequently, supervised learning is employed to finetune the network parameters, mitigating issues related to random parameter initialization and local optima. Experimental analysis indicates that the CFO estimation accuracy significantly improves with the DBN-based CFO estimation technique in high-dynamic environments. Moreover, there is a notable enhancement in Bit Error Rate (BER) performance.

源语言英语
主期刊名Proceedings - 2023 International Conference on Information Processing and Network Provisioning, ICIPNP 2023
出版商Institute of Electrical and Electronics Engineers Inc.
495-499
页数5
ISBN(电子版)9798350363470
DOI
出版状态已出版 - 2023
活动2023 International Conference on Information Processing and Network Provisioning, ICIPNP 2023 - Beijing, 中国
期限: 26 10月 202327 10月 2023

出版系列

姓名Proceedings - 2023 International Conference on Information Processing and Network Provisioning, ICIPNP 2023

会议

会议2023 International Conference on Information Processing and Network Provisioning, ICIPNP 2023
国家/地区中国
Beijing
时期26/10/2327/10/23

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