Deep Learning with Persistent Homology for Orbital Angular Momentum (OAM) Decoding

TL;DR

This paper introduces a novel method combining persistent homology and CNNs to improve orbital angular momentum mode decoding in free-space optical communications, especially under atmospheric turbulence.

Contribution

It proposes a new topological feature extraction approach using persistent homology integrated with CNNs for more accurate OAM mode classification.

Findings

Achieves up to 20% higher classification accuracy than CNN-only methods.

Demonstrates the importance of topological features in OAM mode decoding.

Shows robustness of the method under atmospheric turbulence conditions.

Abstract

Orbital angular momentum (OAM)-encoding has recently emerged as an effective approach for increasing the channel capacity of free-space optical communications. In this paper, OAM-based decoding is formulated as a supervised classification problem. To maintain lower error rate in presence of severe atmospheric turbulence, a new approach that combines effective machine learning tools from persistent homology and convolutional neural networks (CNNs) is proposed to decode the OAM modes. A Gaussian kernel with learnable parameters is proposed in order to connect persistent homology to CNN, allowing the system to extract and distinguish robust and unique topological features for the OAM modes. Simulation results show that the proposed approach achieves up to 20% gains in classification accuracy rate over state-of-the-art of method based on only CNNs. These results essentially show that geometric and topological features play a pivotal role in the OAM mode classification problem.