For existing uniform circular array (UCA)-based orbital angular momentum (OAM) point-to-point communications, the OAM signal can only be detected when the number of antennas of the transmitter and receiver are equal. To break through this barrier, in this letter, we first validate the feasibility of spatial oversampling for OAM reception, which can bring the additional signal-to-noise ratio (SNR) gain at the receiver, thus positively influencing the system's bit error rate (BER). However, more antennas imply the use of high-precision analog-to-digital converters (ADCs) in additional radio frequency (RF) chains, which would result in significant power consumption and costs. Equipping low-cost 1-bit ADCs in the RF chain is a viable approach, but it leads to the deterioration of the OAM receiver's BER. Therefore, we apply comparators and a deep learning (DL)-based detector to the 1-bit quantized OAM receiver to improve the BER. Numerical simulations validate that our proposed OAM receiver can obtain a better BER and reduce the receiver hardware costs.

Low-Cost OAM Spatial Oversampling Receiver With 1-bit Quantized Comparators and DL-Based Detector

Marco Moretti
2024-01-01

Abstract

For existing uniform circular array (UCA)-based orbital angular momentum (OAM) point-to-point communications, the OAM signal can only be detected when the number of antennas of the transmitter and receiver are equal. To break through this barrier, in this letter, we first validate the feasibility of spatial oversampling for OAM reception, which can bring the additional signal-to-noise ratio (SNR) gain at the receiver, thus positively influencing the system's bit error rate (BER). However, more antennas imply the use of high-precision analog-to-digital converters (ADCs) in additional radio frequency (RF) chains, which would result in significant power consumption and costs. Equipping low-cost 1-bit ADCs in the RF chain is a viable approach, but it leads to the deterioration of the OAM receiver's BER. Therefore, we apply comparators and a deep learning (DL)-based detector to the 1-bit quantized OAM receiver to improve the BER. Numerical simulations validate that our proposed OAM receiver can obtain a better BER and reduce the receiver hardware costs.
2024
Chen, Zhihui; Long, Wen-Xuan; Chen, Rui; Moretti, Marco
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1255967
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