基于深度神经网络的低轨卫星信道估计动态映射策略
Deep neural network-based dynamic mapping strategy for low earth orbit satellite channel estimation
- 物联网学报 2026年
- 作者机构：
北京邮电大学电子工程学院，北京 100876
- 作者简介：
崔高峰，cuigaofeng@bupt.edu.cn
- 基金信息：国家自然科学基金(62171052)
DOI：
中图分类号： TN927.2收稿：2025-05-15，
修回：2025-07-23，
录用：2025-08-07，
- 稿件说明：
移动端阅览
刘逸飞, 崔高峰, 潘明宇, 等. 基于深度神经网络的低轨卫星信道估计动态映射策略[J/OL]. 物联网学报, 2026.
LIU Yifei, CUI Gaofeng, PAN Mingyu, et al. Deep neural network-based dynamic mapping strategy for low earth orbit satellite channel estimation[J/OL]. Chinese Journal on Internet of Things, 2026.
摘要
在低轨卫星（LEO
low earth orbit）通信系统中，针对星地链路高速运动引发的时延动态变化以及低信噪比的信道环境，传统的信道估计算法性能表现较差等问题，在正交频分复用（OFDM
Orthogonal Frequency Division Mul-tiplexing）的框架下，提出一种基于深度神经网络（DNN
deep neural networks）与信道响应分块合并结合的低轨卫星通信系统的信道估计算法，可有效提升低轨卫星非地面网络（NTN
non-terrestrial networks）通信环境中数据传输的可靠性与稳定性。该算法将频域导频信道响应矩阵进行分块平均，并结合深度神经网络，在不同时延与噪声的信道场景下，自适应得到导频信道响应分块合并数的最优映射。将该算法与现有的信道估计算法在动态随机时延情况下的误码率性能曲线进行仿真对比。仿真结果表明，该算法相对于传统信道估计算法，在添加动态随机时延情况下，同一信噪比下误码率更低，具有较强的鲁棒性，同时相比于现有的神经网络信道估计算法，能适应较大时延与时延变化情况，具有更强的泛化性。
Abstract
In low-earth-orbit (LEO) satellite communication systems
addressing the poor performance of traditional channel estimation algorithms under dynamic delay variations and low signal-to-noise ratio (SNR) caused by high-speed satellite-terrestrial motion
this paper proposes a deep neural network (DNN)-based channel estimation algorithm combined with sub-block merging of channel responses under an orthogonal frequency-division multiplexing (OFDM) framework. This method enhances the reliability and stability of data transmission in non-terrestrial networks (NTN). The algorithm divides the frequency-domain pilot channel response matrix into sub-blocks for averaging and employs a DNN to adaptively determine the optimal sub-block merging number under different channel conditions with varying delay and noise. The bit error rate (BER) performance of this algorithm is compared through simulations against existing channel estimation methods under dynamically randomized delays. Results demonstrate that the proposed algorithm achieves lower BER at identical SNR levels than traditional methods
exhibiting stronger robustness. Compared to existing neural network-based channel estimation algorithms
it adapts better to larger delays and dynamic delay variations
showing superior generalization capability.
关键词
Keywords
references
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