Phase-Dislocation-Mediated High-Dimensional Fractional Acoustic-Vortex Communication

Ruijie Cao; Gepu Guo; Wei Yue; Yang Huang; Xinpeng Li; Chengzhi Kai; Yuzhi Li; Juan Tu; Dong Zhang; Peng Xi; Qingyu Ma

doi:10.34133/research.0280

Phase-Dislocation-Mediated High-Dimensional Fractional Acoustic-Vortex Communication

Research (Wash D C). 2023 Dec 1:6:0280. doi: 10.34133/research.0280. eCollection 2023.

Authors

Ruijie Cao^{1

2

3}, Gepu Guo¹, Wei Yue¹, Yang Huang^{1

4}, Xinpeng Li¹, Chengzhi Kai¹, Yuzhi Li¹, Juan Tu⁴, Dong Zhang⁴, Peng Xi^{2

3}, Qingyu Ma¹

Affiliations

¹ School of Computer and Electronic Information, Nanjing Normal University, Nanjing 210023, China.
² Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing 100871, China.
³ National Biomedical Imaging Center, Peking University, Beijing 100871, China.
⁴ Institute of Acoustics, Nanjing University, Nanjing 210093, China.

Abstract

With unlimited topological modes in mathematics, the fractional orbital angular momentum (FOAM) demonstrates the potential to infinitely increase the channel capacity in acoustic-vortex (AV) communications. However, the accuracy and stability of FOAM recognition are still limited by the nonorthogonality and poor anti-interference of fractional AV beams. The popular machine learning, widely used in optics based on large datasets of images, does not work in acoustics because of the huge engineering of the 2-dimensional point-by-point measurement. Here, we report a strategy of phase-dislocation-mediated high-dimensional fractional AV communication based on pair-FOAM multiplexing, circular sparse sampling, and machine learning. The unique phase dislocation corresponding to the topological charge provides important physical guidance to recognize FOAMs and reduce sampling points from theory to practice. A straightforward convolutional neural network considering turbulence and misalignment is further constructed to achieve the stable and accurate communication without involving experimental data. We experimentally present that the 32-point dual-ring sampling can realize the 10-bit information transmission in a limited topological charge scope from ±0.6 to ±2.4 with the FOAM resolution of 0.2, which greatly reduce the divergence in AV communications. The infinitely expanded channel capacity is further verified by the improved FOAM resolution of 0.025. Compared with other milestone works, our strategy reaches 3-fold OAM utilization, 4-fold information level, and 5-fold OAM resolution. Because of the extra advantages of high dimension, high speed, and low divergence, this technology may shed light on the next-generation AV communication.