Modulation Recognition Algorithm Based on Transfer Meta-Learning

doi:10.12382/bgxb.2022.0583

Abstract

Abstract:

For the problem that the modulation recognition algorithm based on deep learning can not be trained when there are only a few labeled signal samples, the model-agnostic meta-learning algorithm is used to improve the generalization performance of the network so that the network can accurately recognize the signals to be recognized with only a few training samples. At the same time, the deep neural network is pre-trained to reduce the training difficulty of the network at the meta learning stage. According to the idea of transfer learning, the amount of network parameters required for learning new class signals is reduced by introducing the learnable scaling offset parameters to migrate the network parameters obtained from the pre training. When facing the recognition task of new class signals, the accurate recognition can be achieved by finely tuning the network through a small number of signal samples. The experimental results show that the algorithm can achieve a recognition accuracy of 93.5% when there are only 5 training samples.

Key words: modulation recognition, model-agnostic meta-learning, transfer learning, deep neural network

CLC Number:

TN975

PANG Yiqiong, XU Hua, ZHANG Yue, ZHU Huali, PENG Xiang. Modulation Recognition Algorithm Based on Transfer Meta-Learning[J]. Acta Armamentarii, 2023, 44(10): 2954-2963.

Figures/Tables 12

References 21

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训练集、验证集与测试剂	信号调制样式
训练集	OOK、8ASK、QPSK、16PSK、16APSK、64APSK、16QAM、64QAM、256QAM、AM-SSB-SC
验证集	128QAM、AM-SSB-WC、FM、OQPSK、GMSK、128APSK、BPSK
测试集	4ASK、8PSK、32PSK、32APSK、32QAM、AM-DSB-SC、AM-DSB-WC

训练集、验证集与测试剂	信号调制样式
训练集	OOK、8ASK、QPSK、16PSK、16APSK、64APSK、16QAM、64QAM、256QAM、AM-SSB-SC
验证集	128QAM、AM-SSB-WC、FM、OQPSK、GMSK、128APSK、BPSK
测试集	4ASK、8PSK、32PSK、32APSK、32QAM、AM-DSB-SC、AM-DSB-WC

方法	特征提取网络	5-way
方法	特征提取网络	1-shot	5-shot	20-shot
MAML	ConvNet	50.44±0.84	69.62±0.71	79.05±0.86
	ResNet	27.69±0.45	40.45±0.78	41.53±0.57
MAML+迁移预训练	ConvNet	47.32±0.42	68.21±0.65	77.92±0.51
	ResNet	82.44±0.74	91.26±0.5	92.74±0.47
MAML+迁移预训练+[ψ₁,ψ₂](本文算法)	ConvNet	48.56±0.34	70.38±0.24	79.82±0.28
	ResNet	85.1±0.72	93.5±0.49	95.88±0.36

方法	特征提取网络	5-way
方法	特征提取网络	1-shot	5-shot	20-shot
MAML	ConvNet	50.44±0.84	69.62±0.71	79.05±0.86
	ResNet	27.69±0.45	40.45±0.78	41.53±0.57
MAML+迁移预训练	ConvNet	47.32±0.42	68.21±0.65	77.92±0.51
	ResNet	82.44±0.74	91.26±0.5	92.74±0.47
MAML+迁移预训练+[ψ₁,ψ₂](本文算法)	ConvNet	48.56±0.34	70.38±0.24	79.82±0.28
	ResNet	85.1±0.72	93.5±0.49	95.88±0.36

元学习算法	特征提取网络	5-way 5-shot	5-way 1-shot
PN算法	ConvNet	69.16±0.34	62.93±0.56
	ResNet	84.89±0.28	74.34±0.38
RN算法	ConvNet	70.73±0.42	64.03±0.57
	ResNet	85.52±0.75	75.53±0.68
MN算法	ConvNet	63.34±0.23	58.34±0.26
	ResNet	75.02±0.42	69.93±0.45
SN算法	ConvNet	60.34±0.52	43.52±0.74
	ResNet	67.32±0.56	55.43±0.46
Meta-learner LSTM算法	ConvNet	62.34±0.62	54.37±0.53
	ResNet	72.36±0.43	63.33±0.52
本文算法	ConvNet	68.23±0.54	55.32±0.39
	ResNet	93.5±0.49	85.1±0.72