Modulation format recognition using CNN-based transfer learning models

doi:10.1007/s11082-022-04454-5

Modulation format recognition using CNN-based transfer learning models

2023-02-20

6

OA

AI

S

Safie El-Din Nasr Mohamed *

El‐Sayed M. El‐Rabaie

F

Fathi E. Abd El‐Samie

DOI：10.1007/s11082-022-04454-5

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Abstract

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Transfer learning (TL) appears to be a potential method for transferring information from general to specialized activities. Unfortunately, experimenting using various TL models does not yield good results. In this paper, we propose a model built from scratch with the Hough transform (HT) of constellation diagrams to improve modulation format recognition. The HT is utilized to project points on the constellation diagrams on the Hough space. The HT translates constellation diagram points into lines. Features can then be extracted from the HT domain. Constellation diagrams for eight different modulation formats (2/4/8/16-PSK and 8/16/32/64-QAM) are obtained at optical signal-to-noise ratios (OSNRs) ranging from 5 to 30 dB. The proposed system is based on classification and TL. The obtained results indicate that even at low OSNR values, the proposed system can blindly recognize the wireless optical modulation format with a classification accuracy of up to 99%.

Keywords:

Modulation format recognition (MFR)

Hough transform (HT)

Convolutional neural network (CNN)

Transfer learning (TL)

Modulation format recognition using CNN-based transfer learning models

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