3D Adaptive Beamforming Approach with a Fine-Tuned Deep Neural Network

Ioannis Mallioras, Traianos V. Yioultsis, Nikolaos V. Kantartzis, Pavlos I. Lazaridis, Zaharias D. Zaharis

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


Adaptive beamforming is an essential smart antenna operation, with increasing importance for beyond 5G networks. However, the increased computational complexity of deterministic beamforming algorithms highlights their inability to respond to future demands. In this paper we approach the problem of mapping the angles of arrival of incoming signals on an 8x8 uniform planar array, to a desired complex feeding weight vector. We investigate a well-known deep learning model, the multi-layer perceptron, and we train it on a large dataset produced by the null-steering beamforming (NSB) algorithm. In order to propose the most efficient design, we fine tune the hyperparameters and each individual layer of the deep feedforward neural network architecture. The proposed model is able to reach excellent beam-steering accuracy, with signal to interference-plus-noise ratio levels similar to that of NSB, at a significantly lower response time. Finally, we provide a statistical analysis to compare the performance of each beamformer.

Original languageEnglish
Title of host publication2023 12th International Conference on Modern Circuits and Systems Technologies, MOCAST 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9798350321074
ISBN (Print)9798350321081
Publication statusPublished - 17 Jul 2023
Event12th International Conference on Modern Circuits and Systems Technologies - Athens, Greece
Duration: 28 Jun 202330 Jun 2023
Conference number: 12


Conference12th International Conference on Modern Circuits and Systems Technologies
Abbreviated titleMOCAST 2023

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