Radio-Frequency Multi-Mode OAM Detection Based on UCA Samples Learning
This addresses a practical limitation in OAM communications for wireless systems, offering an incremental improvement over classical methods.
The paper tackled the problem of detecting orbital angular momentum (OAM) modes in radio-frequency communications under misalignment conditions, proposing a learning-based method using classifiers like KNN, SVM, and BPNN, with results showing robustness to errors and BPNN achieving the best generalization performance.
Orbital angular momentum (OAM) at radio-frequency provides a novel approach of multiplexing a set of orthogonal modes on the same frequency channel to achieve high spectral efficiencies. However, classical phase gradient-based OAM mode detection methods require perfect alignment of transmit and receive antennas, which greatly challenges the practical application of OAM communications. In this paper, we first show the effect of non-parallel misalignment on the OAM phase structure, and then propose the OAM mode detection method based on uniform circular array (UCA) samples learning for the more general alignment or non-parallel misalignment case. Specifically, we applied three classifiers: K-nearest neighbor (KNN), support vector machine (SVM), and back-propagation neural network (BPNN) to both single-mode and multi-mode OAM detection. The simulation results validate that the proposed learning-based OAM mode detection methods are robust to misalignment errors and especially BPNN classifier has the best generalization performance.