Lightweight parallel denoising network-based OTFS channel estimation algorithm

XIONG Jun; HAN Xueqing; LIU Xiaoran; ZHANG Hui; ZHAO Haitao; WEI Jibo

doi:10.11959/j.issn.2096-3750.2025.00502

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Lightweight parallel denoising network-based OTFS channel estimation algorithm

Theory and Technology | 更新时间：2026-01-04

- Lightweight parallel denoising network-based OTFS channel estimation algorithm
- Chinese Journal on Internet of Things Vol. 9, Issue 4, Pages: 113-124(2025)
- 作者机构：
  
  1.国防科技大学电子科学学院，湖南长沙 410073
  2.南京邮电大学物联网学院，江苏南京 210023
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(U2441226;62371462);Natural Science Foundation of Hunan Province(2025JJ20067);The Science and Technology Innovation Program of Hunan Province(2022RC1093)
- DOI：10.11959/j.issn.2096-3750.2025.00502
  CLC：
- Received：16 April 2025，
  
  Revised：2025-06-23，
  
  Published：10 December 2025
- 稿件说明：
移动端阅览
熊俊,韩雪晴,刘潇然等.基于轻量并行去噪网络的OTFS信道估计算法[J].物联网学报,2025,09(04):113-124.

XIONG Jun,HAN Xueqing,LIU Xiaoran,et al.Lightweight parallel denoising network-based OTFS channel estimation algorithm[J].Chinese Journal on Internet of Things,2025,09(04):113-124.
熊俊,韩雪晴,刘潇然等.基于轻量并行去噪网络的OTFS信道估计算法[J].物联网学报,2025,09(04):113-124. DOI： 10.11959/j.issn.2096-3750.2025.00502.

XIONG Jun,HAN Xueqing,LIU Xiaoran,et al.Lightweight parallel denoising network-based OTFS channel estimation algorithm[J].Chinese Journal on Internet of Things,2025,09(04):113-124. DOI： 10.11959/j.issn.2096-3750.2025.00502.

摘要

正交时频空（OTFS

orthogonal time frequency space）作为6G关键候选技术之一，能够有效对抗双选择性衰落信道的影响。然而，OTFS系统的信道估计一直是学术界研究的重点和难点。近年来，有研究提出了基于深度学习的OTFS信道估计方案，其运用人工智能技术快速捕捉信道变化，但也存在网络规模大、难以满足移动终端轻量化需求的问题。为此，以提高计算效率、降低设备功耗为目标，提出一种基于轻量并行去噪网络的OTFS信道估计算法。该算法结合图像去噪和数据驱动思想，在保留深度学习算法强大的泛化能力的基础上，通过优化网络结构和降低导频功率，降低了移动端的算力成本，为高速移动场景下终端通信的轻量化提供了新的解决方案。所提算法的参数规模仅为现有基于图像去噪的卷积神经网络（DnCNN

denoising convolutional neural network）的15%，大幅降低了网络参数规模和计算复杂度。仿真结果表明，凭借独特的并行结构设计，所提算法弥补了轻量化设计带来的估计性能损失。在五径快时变信道下，所提算法相较于DnCNN实现了4 dB的性能增益。

Abstract

Orthogonal time frequency space (OTFS) as one of the key candidate technologies for 6G

is recognized for its ability to effectively combat the effects of doubly-selective fading channels. However

channel estimation in OTFS systems has remained a major focus and challenge in academic research. In recent years

deep learning-based OTFS channel estimation schemes were proposed

which utilized artificial intelligence techniques to rapidly capture channel variations. Nevertheless

these existing algorithms were generally characterized by large network scales

making it difficult to meet the lightweight requirements of mobile terminals. To address this issue

an OTFS channel estimation algorithm based on a lightweight parallel denoising network was proposed with the aim of improving computational efficiency and reducing device power consumption. By integrating image denoising and data-driven concepts

the algorithm retained the strong generalization capability of deep learning methods while reducing the computational cost on mobile devices through optimized network architecture and reduced pilot power

thereby providing a new solution for lightweight terminal communication in high-mobility scenarios. The parameter quantity of the proposed algorithm was only 15% of that of the existing denoising convolutional neural network (DnCNN)

significantly reducing both the network parameter scale and computational complexity. Simulation results demonstrated that

thanks to its unique parallel structure design

the proposed algorithm compensated for the estimation performance loss caused by lightweight design. Under a five-path fast time-varying channel

a performance gain of 4 dB was achieved compared to DnCNN.

关键词

Keywords

references

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