Abstract
The use of deep learning in the field of wireless communications has already shown great potential. In this work we present a deep feedforward and a deep recurrent neural network trained as null steering beamformers that target high sidelobes in order to establish low sidelobe level for any desired incoming signal. Using of a zero-forcing algorithm, we apply a sidelobe-damping algorithm where iteratively, a constant number of sidelobes is nullified until a desired sidelobe level is reached. In this way, we can create a dataset which we later use to train our NN models. Using Bayesian optimization, we perform hyperparameter tuning to configure structure and training related parameters for the NNs under examination. The NN models are later fine-tuned using a small dataset containing more demanding cases.
Original language | English |
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Title of host publication | 28th European Wireless Conference, EW 2023 |
Publisher | VDE Verlag GmbH |
Pages | 196-201 |
Number of pages | 6 |
ISBN (Electronic) | 9783800762262, 978380062255 |
Publication status | Published - 2 Oct 2023 |
Event | 28th European Wireless Conference - Rome, Italy Duration: 2 Oct 2023 → 4 Oct 2023 Conference number: 28 |
Conference
Conference | 28th European Wireless Conference |
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Abbreviated title | EW 2023 |
Country/Territory | Italy |
City | Rome |
Period | 2/10/23 → 4/10/23 |