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Joint Demodulation and Error Correcting Codes Recognition Using Convolutional Neural Network
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DOI：10.1109/ACCESS.2022.3201354.png)
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Abstract
En 中文
Demodulation of the communication signals and blind identification of error-correcting codes (ECC) are two essential purposes in adaptive modulation and coding (AMC) techniques and non-cooperative communication fields. The existing approaches treat them as two separate problems, namely the demodulation of signals with known a priori knowledge (such as channel state information (CSI) or channel noise) and the ECC type identification that depends on the demodulation results. In this paper, a novel one-stage ECC identification approach based on a multi-task deep convolutional neural network (MT-DCNN) is presented. With this architecture, the proposed method automatically recognizes the ECC types of the baseband in-phase and quadrature-phase (IQ) data without relying on any conventional demodulators. Precisely, the proposed MT-DCNN consists of three modules: the feature extraction module, the demodulation module, and the ECC types recognition module. Experiment results show that the proposed architecture can accurately identify the ECC types of the baseband IQ signals and is superior to those of the existing two-stage recognition approaches.
Keywords:
Demodulation
Convolutional neural networks
Feature extraction
Convolutional codes
Convolutional neural networks
Codes
Error correction codes
Learning systems
Multitasking
Convolutional neural network
demodulation
error-correcting codes identification
multitask learning
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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