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4D Joint Harmonic Retrieval and Model Order Estimation with Convolutional Neural Networks

Schieler S., Semper S., Faramarzahangari R., Schneider C., and Thomä R.

Proceedings of the 5th International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI' 2023), 2023

https://doi.org/10.13140/RG.2.2.27945.77924

Abstract

Harmonic retrieval is essential in radio channel sounding, estimation, and modeling. In our previous work, we proposed a CNN-based approach combined with additional steps on the likelihood function. This paper extends the approach to perform joint 4D harmonic retrieval by utilizing the samples from a multi-antenna receiver in frequency, time, and the spatial domains of a radio channel transfer function. The proposed architecture also reliably estimates the number of spectral components on measurements. Hence, our approach can estimate four-dimensional parameters from a signal without prior knowledge of the unknown number of paths. Therefore, the architecture jointly solves the model order selection problem and the parameter estimation task in 4D

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