首页|基于模型驱动深度学习的OTFS信道估计

基于模型驱动深度学习的OTFS信道估计

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针对单输入单输出(SISO)的正交时频空间(OTFS)调制系统,该文利用一种模型驱动深度学习算法进行OTFS信道估计.该方案首先将去噪近似消息传递(DAMP)算法进行深度展开,利用去噪卷积神经网络代替传统的去噪器,对含噪的时延多普勒信道进行去噪估计,然后提供了状态演化方程来预测可学习去噪近似消息传递(LDAMP)算法的理论归一化均方误差性能.仿真结果表明,相比于其他估计方案,该方案不仅在低信噪比条件下具有优越的性能表现,而且还具有非常好的鲁棒性,在信道路径总数不变时,增加OTFS 2维网格点数量,可以有效提升信道估计精确度.
Orthogonal Time Frequency Space Channel Estimation Based on Model-driven Deep Learning
In this paper, a channel estimation scheme based on model-driven deep learning algorithm is proposed for Single Input Single Output (SISO) Orthogonal Time Frequency Space (OTFS) modulation systems. First, the Denoising Approximate Message Passing (DAMP) algorithm is considerably expanded. Then the traditional denoiser is replaced by the Denoising Convolutional Neural Network (DnCNN) to estimate the delay-Doppler channel with additive white Gaussian noise. The State Evolution (SE) equation is provided to predict the theoretical Normalized Mean Square Error (NMSE) performance of the Learned Denoising based Approximate Message Passing (LDAMP) algorithm. Simulation results show that the scheme performs well under a low Signal-to-Noise Ratio (SNR) and has great robustness compared with other estimation schemes. When the total number of channel paths is invariant, increasing the number of OTFS two-dimensional grid points can effectively improve channel estimation accuracy.

Orthogonal Time Frequency Space(OTFS)Channel estimationDelay Doppler channelDeep learning

蒲旭敏、刘雁翔、宋米雪、陈前斌

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重庆邮电大学通信与信息工程学院 重庆 400065

正交时频空间 信道估计 时延多普勒信道 深度学习

国家自然科学基金中国博士后科学基金江苏省博士后科研资助计划重庆市教委科学技术研究计划重庆市教委科学技术研究计划

617010622019M6516492018K041cKJQN202100649KJQN202000612

2024

电子与信息学报
中国科学院电子学研究所 国家自然科学基金委员会信息科学部

电子与信息学报

CSTPCD北大核心
影响因子:1.302
ISSN:1009-5896
年,卷(期):2024.46(2)
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