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Abstract
Existing matrix-based neural network for direction-of-arrival (DOA) estimation has to train a large amount of parameters proportional to the length of vectorized signal statistics, resulting in a heavy system overload. To address the problem, a tensorized neural layer decomposition-based neural network is proposed for 2-D DOA estimation. In particular, the covariance tensor of tensor signals is propagated to hidden state tensors. The feedforward propagation is formulated as an inverse Tucker decomposition, such that parameters in the tensorized neural layers are compressed into inverse Tucker factors. Accordingly, the tensorized backpropagation procedure is designed for network training. It is proved that the number of parameters is significantly reduced, which leads to a faster training process. Simulation results demonstrate that the proposed method reduces the number of trained parameters by more than 122,000 times compared to the matrix-based neural network while maintaining a moderate accuracy.
Original language | English |
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Title of host publication | ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings |
Publisher | IEEE |
Number of pages | 5 |
ISBN (Electronic) | 978-1-7281-6327-7 |
DOIs | |
Publication status | Published - 2023 |
MoE publication type | A4 Conference publication |
Event | IEEE International Conference on Acoustics, Speech, and Signal Processing - Rhodes Island, Greece Duration: 4 Jun 2023 → 10 Jun 2023 |
Publication series
Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
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Volume | 2023-June |
ISSN (Print) | 1520-6149 |
Conference
Conference | IEEE International Conference on Acoustics, Speech, and Signal Processing |
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Abbreviated title | ICASSP |
Country/Territory | Greece |
City | Rhodes Island |
Period | 04/06/2023 → 10/06/2023 |
Keywords
- Covariance tensor
- DOA estimation
- neural layer decomposition
- tensorized neural network
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AI Based RAN: Towards Scalable and AI-Based Solutions for Beyond-5G Radio Access Networks
Vorobyov, S. (Principal investigator), Esfandiari, M. (Project Member), Hassas Irani, K. (Project Member) & Zhang, T. (Project Member)
01/01/2023 → 31/12/2025
Project: Academy of Finland: Other research funding