Abstract
The recent integration of neural networks into the domain of direction of arrival estimation marks a promising frontier in the landscape of next-generation wireless communications. Our paper meticulously delves into the architecture of the proposed deep convolutional neural network (DCNN), presenting a novel framework designed to streamline the classification process within the output layer. Operating on correlation matrices created by signals received by a 4 × 4 planar antenna array, our DCNN predicts angles of arrival in 3D space. We assess the model’s performance in scenarios involving the simultaneous reception of signals, employing the mean absolute error as a metric to gauge prediction errors in the angle domain. The simulation results affirm the superior performance of the proposed deep learning-based scheme. The model’s robustness is rigorously examined across various validation cases, providing conclusive evidence of its potential in real-world applications.
Original language | English |
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Title of host publication | 2024 13th International Conference on Modern Circuits and Systems Technologies, MOCAST 2024 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Number of pages | 4 |
ISBN (Electronic) | 9798350385427 |
ISBN (Print) | 9798350385434 |
DOIs | |
Publication status | Published - 6 Aug 2024 |
Event | 13th International Conference on Modern Circuits and Systems Technologies - Sofia, Bulgaria Duration: 26 Jun 2024 → 28 Jun 2024 Conference number: 13 |
Publication series
Name | International Conference on Modern Circuits and Systems Technologies |
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Publisher | IEEE |
Volume | 2024 |
ISSN (Print) | 2993-4435 |
ISSN (Electronic) | 2993-4443 |
Conference
Conference | 13th International Conference on Modern Circuits and Systems Technologies |
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Abbreviated title | MOCAST 2024 |
Country/Territory | Bulgaria |
City | Sofia |
Period | 26/06/24 → 28/06/24 |