Orbital Angular Momentum Mode Intelligent Identification in the Secondary Frequency Domain with Compressive Sensing

Abstract

The Electro-Magnetic (EM) waves with Orbital Angular Momentum (OAM) can achieve high spectral efficiency by multiplexing different OAM modes. In order to effectively identify the OAM modes received in partial phase plane, different modes are mapped to the frequency shifts in the secondary frequency domain. The high-speed acquisition equipment is necessary for the traditional method in the process of receiving Radio Frequency (RF) or Intermediate Frequency (IF) signals, which suffers from a high cost. However, Compressive Sensing (CS) can break the Nyquist sampling restriction by random observation and is expected to build the relationship between the received signal and the frequency shift in the secondary frequency domain at a lower sampling rate, so that the cost is low. Moreover, due to the existence of multipath effect, the transfer learning is employed to establish spectrum-mode mapping, further improves the Bit Error Rate (BER) performance and the transmission distance. Therefore, this paper proposes an intelligent OAM mode identification method based on CS and transfer learning. At the same time, the random sampling is carried out based on the Analog-to-Information Converter (AIC) to realize the OAM mode identification with the low sampling rate. The simulation results can verify the validity and efficiency of this method.