Channel estimation algorithm for OTFS based on basis expansion model with bidirectional LSTM network

Huang Ziyi; Li Guoquan; Lin Jinzhao; Pang Yu; Wu Ruiheng

doi:10.11959/j.issn.1000-436x.TXXB260143

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Channel estimation algorithm for OTFS based on basis expansion model with bidirectional LSTM network

更新时间：2026-04-30

- Channel estimation algorithm for OTFS based on basis expansion model with bidirectional LSTM network
- Journal on Communications (2026)
- 作者机构：
  
  1.重庆邮电大学通信与信息工程学院，重庆 400065
  2.伦敦布鲁内尔大学电子与电气工程系，英国伦敦UB8 3PH
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(U21A20447;12411530119);The Chongqing Natural Science Foundation(CSTB2022NSCQ-LZX0069);The Chengdu Science and Technology Program(2025-YF11-00077-HZ)
- DOI：10.11959/j.issn.1000-436x.TXXB260143
  CLC： TN92
- Received：24 March 2026，
  
  Revised：2026-04-29，
  
  Accepted：30 April 2026，
- 稿件说明：
移动端阅览
Huang Ziyi, Li Guoquan, Lin Jinzhao, et al. Channel estimation algorithm for OTFS based on basis expansion model with bidirectional LSTM network[J/OL]. Journal on Communications, 2026.
DOI：

Huang Ziyi, Li Guoquan, Lin Jinzhao, et al. Channel estimation algorithm for OTFS based on basis expansion model with bidirectional LSTM network[J/OL]. Journal on Communications, 2026. DOI： 10.11959/j.issn.1000-436x.TXXB260143.

摘要

高精度的信道估计是正交时频空间（OTFS）系统在6G高速移动场景中可靠应用的关键。为进一步提升OTFS系统的信道估计精度，提出了一种基于融合基扩展模型（BEM）和双向长短期记忆（LSTM）网络的OTFS信道估计算法。首先利用BEM将延时-多普勒（DD）域的信道估计问题转换为基系数估计问题，然后引入双向LSTM网络对基系数进行离线训练与在线预测，并结合自注意力（SA）机制增强对时序特征的全局感知能力，实现基系数的高精度估计与动态跟踪。在此基础上，进一步设计了一种低复杂度两级均衡器，基于估计得到的基系数重构信道信息，通过单抽头均衡器消除多径效应，并在DD域采用基于对数似然比的迭代干扰消除（LLR-IIC）算法抑制多普勒扩展引起的残余干扰。仿真结果表明，所提算法在信道估计精度、泛化能力和复杂度方面均具有明显优势；结合所提均衡器后，可在保持较低复杂度情况下，实现更优的误比特率（BER）性能。

Abstract

Accurate channel estimation is critical for the reliable deployment of orthogonal time frequency space (OTFS) systems in 6G high-mobility scenarios. To further improve the channel estimation accuracy in OTFS systems

this paper proposes a channel estimation algorithm integrating the basis expansion model (BEM) and a bidirectional long short-term memory (LSTM) network. The proposed algorithm first uses BEM to reformulate the channel estimation problem in the delay-Doppler (DD) domain into a basis coefficient estimation problem. A bidirectional LSTM network is then introduced for offline training and online prediction of the basis coefficients

and a self-attention (SA) mechanism is incorporated to strengthen global perception of temporal features

enabling high-accuracy estimation and dynamic tracking of the basis coefficients. Building on this

a low-complexity two-stage equalizer is further designed. Channel information is reconstructed from the estimated basis coefficients

multipath effects are mitigated via a single-tap equalizer

and a log likelihood ratio based iterative interference cancellation (LLR-IIC) algorithm is employed in the DD domain to suppress residual interference induced by Doppler spread. Simulation results demonstrate that the proposed algorithm offers significant advantages in channel estimation accuracy

generalization capability

and complexity. When combined with the proposed equalizer

superior bit error rate (BER) performance is achieved while maintaining low complexity.

关键词

Keywords

references

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