DOA Estimation for 6G Communication Systems

Haya Al Kassir, Ioannis T. Rekanos, Pavlos I. Lazaridis, Traianos V. Yioultsis, Nikolaos V. Kantartzis, Christos S. Antonopoulos, George K. Karagiannidis, Zaharias D. Zaharis

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


The objective of this study is to analyze and compare different neural network (NN) architectures as multi-class classifiers to estimate the direction of arrival (DOA) using a uniform linear array (ULA). The study specifically investigates the prediction skills of three NNs: feed forward NN (FFNN), convolutional NN (CNN), and residual NN (ResNet) when estimating incoming signal DOAs in a realistic ULA of (M = 16) elements under noisy conditions. The NNs are trained on a correlation matrix generated by a ULA to estimate the DOAs. The results of the simulations indicate that ResNet performs better than FFNN and CNN in accurately estimating incoming signals.

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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