arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2107.07161cs.ITeess.SPmath.IT
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基于深度学习的OFDM频时分割和注意力机制信道估计

Deep Learning Based OFDM Channel Estimation Using Frequency-Time Division and Attention Mechanism

Ang Yang, Peng Sun, Tamrakar Rakesh, Bule Sun, Fei Qin

摘要:

在本文中,我们提出了一种频分-时分网络(FreqTimeNet),以提高基于深度学习(DL)的OFDM信道估计的性能。该FreqTimeNet基于频域和时域之间的正交性设计。在FreqTimeNet中,输入由并行频率块和并行时间块顺序处理。通过引入利用信噪比信息的注意力机制,提出了一种基于注意力的频率时间网(AttenFreqTimeNet)。利用第三代合作伙伴计划(3GPP)信道模型,在不同的场景下的均方误差(MSE)的FreqTimeNet和AttenFreqTimeNet的性能进行了评估。提出了一种构造混合训练数据的方法,解决了DL中的泛化问题。据观察,AttenFreqTimeNet优于FreqTimeNet,FreqTimeNet优于其他DL网络具有合理的复杂性。

英文摘要:

In this paper, we propose a frequency-time division network (FreqTimeNet) to improve the performance of deep learning (DL) based OFDM channel estimation. This FreqTimeNet is designed based on the orthogonality between the frequency domain and the time domain. In FreqTimeNet, the input is processed by parallel frequency blocks and parallel time blocks sequentially. By introducing the attention mechanism using the SNR information, an attention based FreqTimeNet (AttenFreqTimeNet) is proposed. Using 3rd Generation Partnership Project (3GPP) channel models, the mean square error (MSE) performance of FreqTimeNet and AttenFreqTimeNet under different scenarios is evaluated. A method for constructing mixed training data is proposed, which could address the generalization problem in DL. It is observed that AttenFreqTimeNet outperforms FreqTimeNet, and FreqTimeNet outperforms other DL networks with reasonable complexity.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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