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

LIAO Xi; HE Xiangni; ZHANG Zhe; WANG Yang

doi:10.11999/JEIT260075

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LIAO Xi, HE Xiangni, ZHANG Zhe, WANG Yang. Channel Estimation for MIMO-OFDM Based on Adaptive Transformer Network in High-speed Mobile Scenarios[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260075

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LIAO Xi, HE Xiangni, ZHANG Zhe, WANG Yang. Channel Estimation for MIMO-OFDM Based on Adaptive Transformer Network in High-speed Mobile Scenarios[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260075

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

doi: 10.11999/JEIT260075 cstr: 32379.14.JEIT260075

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)

Received Date: 2026-01-21
Accepted Date: 2026-07-13
Rev Recd Date: 2026-07-13

Available Online: 2026-07-24

Abstract

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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References(19)

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