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.