Autors: Mallioras, I., Zaharis, Z.D., Lazaridis, P.I., Poulkov, V. K., Kantartzis, N.V., Yioultsis, T.V.
Title: An Adaptive Beamforming Approach Applied to Planar Antenna Arrays Using Neural Networks
Keywords: Adaptive beamforming; deep learning; gated recurrent units; long short-Term memory; neural networks; null steering beamforming; planar antenna arrays; recurrent neural networks

Abstract: Future wireless networks depend on the improvement of current smart antenna operations so that they maintain high accuracy levels at low response times. Utilizing machine learning techniques, it is possible to replace the currently used algorithms with a much faster yet reliable alternative. In this study, we focus on adaptive beamforming applied to a planar antenna array using the null steering beamforming algorithm (NSB). We test different types of deep neural networks (DNNs) as potential alternative beamformers, by comparing their accuracy to that of the NSB algorithm. The application concerns an 8×8 planar antenna array composed of isotropic elements. The DNNs tested here are the traditional feedforward neural networks and recurrent neural networks using either gated recurrent units or long short-Term memory units. In addition, we investigate each DNN type to make sure we are utilizing the best version of each neural network architecture.

References

    Issue

    in Proceedings of IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Sofia, Bulgaria, 06-09 June 2022, pp. 293-297, 2022, Bulgaria, Institute of Electrical and Electronics Engineers Inc., DOI 10.1109/BlackSeaCom54372.2022.9858302

    Copyright IEEE

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus и Web of Science