Deep learning-based direction finding in the presence of direction-dependent mutual coupling

In this paper, the problem of direction finding is considered for millimeter-wave communication with direction-dependent mutual coupling. For direction-of-arrival estimation, a low-complexity deep neural network (DNN)-based regression model is proposed, taking an array output as the DNN input with a straightforward preprocessing step. In addition to designing DNN building blocks, a quantitative analysis is performed to enhance the generalization capability of the DNN trained with finite-size training data. A theoretical performance bound is derived in terms of the Crame & PRIME;r-Rao lower bound (CRLB). The effectiveness of the proposed approach is verified by comparison with existing methods and the CRLB.& COPY; 2022 The Authors. Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).