高速移动场景下基于自适应Transformer网络的MIMO-OFDM信道估计

廖希; 何香妮; 张哲; 王洋

doi:10.11999/JEIT260075

高速移动场景下基于自适应Transformer网络的MIMO-OFDM信道估计

doi: 10.11999/JEIT260075 cstr: 32379.14.JEIT260075

廖希^1,,
何香妮¹,
张哲^2, ,,
王洋¹

1.
重庆邮电大学通信与信息工程学院重庆 400065
2.
中国人民解放军31401部队长春 130012

基金项目: 重庆市自然科学基金(CSTB2025YITP-QCRC0045)

详细信息

作者简介:
廖希：女，博士，教授，研究方向为面向6G的信道测量与建模、广域无线通信等

何香妮：女，硕士生，研究方向为OFDM系统信道估计

张哲：男，硕士，工程师，研究方向为无线通信

王洋：男，博士，教授，研究方向为第6代移动通信技术、毫米波太赫兹测量与建模、涡旋电磁波和智能反射面等

通讯作者:
张哲　19628069@qq.com

中图分类号: TN911.23
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出版历程
- 收稿日期: 2026-01-21
- 修回日期: 2026-07-13
- 录用日期: 2026-07-13
- 网络出版日期: 2026-07-24

Channel Estimation for MIMO-OFDM Based on Adaptive Transformer Network in High-speed Mobile Scenarios

LIAO Xi^1
,,
HE Xiangni¹,
ZHANG Zhe^{2
, ,},
WANG Yang¹

1.
College of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
PLA Unit 31401, Changchun 130012, China

Funds: Chongqing Natural Science Foundation (CSTB2025YITP-QCRC0045)

摘要

摘要: 高速移动场景下显著多普勒频移引发信道快速时变特性及子载波间干扰加剧，导致MIMO-OFDM系统信道估计精度下降及鲁棒性不足，该文提出一种基于特征线性调制(FiLM)的自适应Transformer信道估计模型——AdaFiT。首先，采用可分离二维线性上采样获取全时频域信道估计，并利用点卷积在通道维度对多天线复数信道的实部与虚部进行联合建模，结合卷积特征增强模块提取多尺度时频特征；然后，设计信道自适应特征调制模块，将信道参数作为先验条件信息，基于FiLM机制生成特征层面的缩放系数与偏移系数，仿射调制Transformer网络的块嵌入序列，实现对信道统计变化的条件自适应；最后，通过二维可学习位置编码的Transformer编码器全局建模块级特征，并结合残差重建结构融合局部与全局特征，完成高精度信道重建。结果表明，所提AdaFiT模型在C型簇延迟线信道(CDL-C)和A型簇延迟线信道(CDL-A)两种信道模型下均优于最小二乘双线性插值(LS-Blinear)、线性最小均方误差(LMMSE)及现有自适应方法，对不同信道模型具有较好的适应性。其中，在CDL-C信道模型下的动态信噪比、多普勒频移及不同时延扩展条件下，均方根误差最大可提升7 dB。
- MIMO-OFDM系统 /
- 信道估计 /
- Transformer网络 /
- 自适应特征调制 /
- 特征线性调制
Abstract: Objective Accurate Channel State Information (CSI) is essential for coherent detection, beamforming, and adaptive resource allocation in Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems. In high-mobility scenarios, large Doppler shifts and multipath propagation jointly produce doubly selective fading, destroy subcarrier orthogonality, and intensify inter-carrier interference. Therefore, conventional Least Squares (LS) and Linear Minimum Mean Square Error (LMMSE) estimators exhibit substantial performance degradation. Existing deep learning-based channel estimation methods provide strong nonlinear modeling capability but often lack sufficient adaptability to variations in Signal-to-Noise Ratio (SNR), delay spread, and maximum Doppler shift. Moreover, channel physical parameters are not efficiently exploited as prior information. To address these limitations, this paper proposes AdaFiT, an adaptive Transformer-based channel estimation network incorporating Feature-wise Linear Modulation (FiLM) for high-mobility MIMO-OFDM systems. Methods AdaFiT performs channel estimation by jointly exploiting local time-frequency features, global time-frequency dependencies, and channel-adaptive feature modulation. The network takes LS estimates at pilot positions together with SNR, delay spread, and maximum Doppler shift as inputs. A separable two-dimensional linear upsampling module first reconstructs sparse pilot estimates over the complete OFDM time-frequency grid by independently processing the real and imaginary components in the frequency and time dimensions. A convolutional feature enhancement module then extracts robust local feature representations. Specifically, a complex feature-mixing layer jointly models the real and imaginary components of multi-antenna complex channel responses, while multi-scale convolutional blocks with channel attention capture local time-frequency features and suppress noise. Subsequently, an FiLM-based channel-adaptive feature modulation module embeds the three channel physical parameters through independent multilayer perceptrons and combines them into a channel-condition representation. The resulting representation generates feature-wise scaling and shifting coefficients to dynamically recalibrate block-embedded feature sequences according to changing channel statistical characteristics. Finally, the modulated feature sequences are processed by a Transformer encoder with learnable two-dimensional positional encoding to capture long-range dependencies across the time-frequency grid and antenna dimensions. A residual reconstruction module combines global and local feature representations to generate accurate channel estimates while preserving fine local details. Results and Discussions Simulation results are obtained under the CDL-C and CDL-A channel models. The LS-based bilinear interpolation method, LMMSE, AdaFortiTran, and AdaFiT without the channel-adaptive feature modulation module are selected as benchmark methods. Their Mean Squared Error (MSE) performance is evaluated under different SNR, maximum Doppler shift, and delay spread conditions. Under the CDL-C channel model, AdaFiT achieves the lowest MSE across the entire SNR range (Fig. 3). At an SNR of 0 dB, the MSE is approximately 2.5 dB lower than that of AdaFortiTran, and the performance gain increases to approximately 7 dB at an SNR of 30 dB. Compared with AdaFiT without the channel-adaptive feature modulation module, the proposed model achieves a maximum MSE improvement of approximately 2.1 dB, confirming the effectiveness of the proposed channel-adaptive feature modulation module. When the maximum Doppler shift increases from 200 Hz to 1 400 Hz, AdaFiT consistently achieves the lowest MSE (Fig. 4). In the high-Doppler region (1 000～1 400 Hz), AdaFiT outperforms AdaFiT without the channel-adaptive feature modulation module and AdaFortiTran by approximately 2 dB and 3.5 dB, respectively, demonstrating superior robustness under rapidly time-varying channel conditions. For delay spreads ranging from 100 ns to 700 ns, AdaFiT also achieves the lowest MSE throughout the entire range (Fig. 5), providing gains of approximately 3 dB and 5.5 dB over AdaFiT without the channel-adaptive feature modulation module and AdaFortiTran, respectively. These results demonstrate that the proposed channel-adaptive feature modulation mechanism effectively improves model adaptability to time-selective and frequency-selective fading. To further evaluate the generalization capability of AdaFiT, additional simulations are conducted under the 3GPP CDL-A channel model. At SNRs of 0～5 dB, AdaFiT outperforms LMMSE by approximately 4～5 dB, and the performance gain increases to approximately 7 dB at SNRs of 20～30 dB (Fig. 6(a)). Compared with AdaFortiTran, AdaFiT achieves an MSE gain of approximately 2 dB under low-SNR conditions, which increases to approximately 5.5 dB under high-SNR conditions. In the maximum Doppler shift experiment, the MSE of AdaFiT remains between approximately –38 dB and –37 dB over the range of 200～800 Hz and is approximately 5 dB lower than that of AdaFortiTran (Fig. 6(b)). Although the MSE increases when the maximum Doppler shift exceeds 1 000 Hz, AdaFiT still provides an approximately 6 dB gain over LMMSE at 1 400 Hz and continues to outperform AdaFortiTran. Across the entire delay spread range, AdaFiT achieves gains of approximately 4～6 dB over LMMSE and approximately 4～5.5 dB over AdaFortiTran (Fig. 6(c)). Conclusions AdaFiT, an adaptive Transformer-based channel estimation network, jointly exploits local time-frequency features, global time-frequency dependencies, and channel-adaptive feature modulation to improve channel estimation accuracy. Simulation results under the CDL-C and CDL-A channel models demonstrate that AdaFiT consistently achieves lower MSE than the LS-based bilinear interpolation method, LMMSE, AdaFortiTran, and AdaFiT without the channel-adaptive feature modulation module under different SNR, maximum Doppler shift, and delay spread conditions. These results confirm the effectiveness of the proposed channel-adaptive feature modulation mechanism and demonstrate that AdaFiT maintains high estimation accuracy and stable performance across different channel models, indicating strong adaptability to dynamic channel environments.

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图 1 导频模式($ {N}_{\text{t}}=4,D=12,{N}_{\text{p}}=2 $)

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图 2 AdaFiT模型架构及子模块

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图 3 不同信噪比下各模型的MSE

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图 4 不同最大多普勒频移下各模型的MSE

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图 5 不同时延扩展下各模型的MSE

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图 6 CDL-A信道下各模型在不同信道条件下的MSE性能对比

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图 7 先验信息失配对AdaFiT信道估计性能的影响

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表 1 模型复杂度对比

模型	参数量(M)	复杂度
LS-Bilinear	−	$ O\left({N}_{\text{tx}}{N}_{\text{rx}}{N}_{\text{f}}{N}_{\text{t}}\right) $
LMMSE	−	$ O\left({N}_{\text{tx}}{N}_{\text{rx}}{N}_{\text{f}}{}^{3}{N}_{\text{t}}\right) $
AdaFortiTran	2.738	$ O\left({({{N}_{\text{tx}}}{{N}_{\text{rx}}})}^{2}{N}_{\text{f}}{N}_{\text{t}}+{S}^{2}(D+k)+S{(D+k)}^{2}\right) $
AdaFiT(无自适应模块)	0.268	$ O\left({({{N}_{\text{tx}}}{{N}_{\text{rx}}})}^{2}{N}_{\text{f}}{N}_{\text{t}}+{S}^{2}D+S{D}^{2}\right) $
AdaFiT(本文模型)	0.958	$ O\left({({{N}_{\text{tx}}}{{N}_{\text{rx}}})}^{2}{N}_{\text{f}}{N}_{\text{t}}+{S}^{2}D+S{D}^{2}+({N}_{\text{tx}}{N}_{\text{rx}}){N}_{\text{f}}{N}_{\text{t}}\right) $

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表 2 MIMO-OFDM系统参数

参数	符号	数值	参数	符号	数值
发射天线数	$ {N}_{\text{tx}} $	4	接收天线数	$ {N}_{\text{rx}} $	4
载波频率	$ {f}_{\text{c}} $	3.5 GHz	信道模型	−	CDL-C; CDL-A
子载波间隔	$ {\Delta }f $	15 kHz	系统带宽	$ B $	5 MHz
OFDM符号数	$ {N}_{\text{t}} $	14	总子载波数	$ {N}_{\text{f}} $	300
单天线导频数	$ {N}_{\text{p}} $	50	导频间隔	$ D $	12
数据调制	−	QPSK	信噪比	$ s $	[0, 5, 10, 15, 20, 25, 30] dB
时延扩展	$ {\tau }_{\text{ds}} $	[50, 150,···, 650] ns	最大多普勒频移	$ {f}_{\text{d}} $	[100, 200,···, 1000] Hz

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