基于深度卷积神经网络的无网格谐波恢复与模型阶数选择
Grid-free Harmonic Retrieval and Model Order Selection using Deep Convolutional Neural Networks
AI总结：
针对未知路径数的无线电信道二维参数估计问题，提出一种准无网格深度学习谐波恢复方法，可联合完成模型阶数选择与参数估计，结合多通道加窗提升鲁棒性，性能优于现有方法且可用于最大似然估计器的高效初始化。
摘要：
谐波恢复技术是无线电信道探测、估计与建模的基础。本文提出一种深度学习方法，可从无线电信道传递函数的频率和时间采样中联合估计时延与多普勒参数。本研究从路径数量未知的信号中估计二维参数。与现有基于深度学习的方法相比，该方法并非通过分类估计信号参数，而是以准无网格方式实现，从而缓解了基于网格的方法产生的偏差、频谱泄漏和虚假目标问题。所提出的架构还能可靠估计测量中的路径数量，因此可同时解决模型阶数选择与参数估计任务。此外，我们提出对数据进行多通道加窗处理，以提升估计器的鲁棒性。我们还将该方法的性能与其他谐波恢复方法进行了对比，并将其集成到现有的最大似然估计器中，为基于梯度的迭代提供高效初始化。
英文摘要：
Harmonic retrieval techniques are the foundation of radio channel sounding, estimation, and modeling. This paper introduces a Deep Learning approach for joint delay- and Doppler estimation from frequency and time samples of a radio channel transfer function. Our work estimates the two-dimensional parameters from a signal containing an unknown number of paths. Compared to existing deep learning-based methods, the signal parameters are not estimated via classification but in a quasi-grid-free manner. This alleviates the bias, spectral leakage, and ghost targets that grid-based approaches produce. The proposed architecture also reliably estimates the number of paths in the measurement. Hence, it jointly solves the model order selection and parameter estimation task. Additionally, we propose a multi-channel windowing of the data to increase the estimator's robustness. We also compare the performance to other harmonic retrieval methods and integrate it into an existing maximum likelihood estimator for efficient initialization of a gradient-based iteration.
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。
