Zero Forcing Beamforming With Sidelobe Suppression Using Neural Networks

Ioannis Mallioras, Traianos V. Yioultsis, Nikolaos V. Kantartzis, Pavlos I. Lazaridis, Atanas Vlahov, Vladimir Poulkov, Zaharias D. Zaharis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 languageEnglish
Title of host publication28th European Wireless Conference, EW 2023
PublisherVDE Verlag GmbH
Number of pages6
ISBN (Electronic)9783800762262, 978380062255
Publication statusPublished - 2 Oct 2023
Event28th European Wireless Conference - Rome, Italy
Duration: 2 Oct 20234 Oct 2023
Conference number: 28


Conference28th European Wireless Conference
Abbreviated titleEW 2023

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