arXiv:1811.06103
Deep Neural Networks based Modrec: Some Results with Inter-Symbol Interference and Adversarial Examples
원문 초록
Recent successes and advances in Deep Neural Networks (DNN) in machine vision and Natural Language Processing (NLP) have motivated their use in traditional signal processing and communications systems. In this paper, we present results of such applications to the problem of automatic modulation recognition. Variations in wireless communication channels are represented by statistical channel models and their parameterization will increase with the advent of 5G. In this paper, we report effect of simple two path channel model on our naive deep neural network based implementation. We also report impact of adversarial perturbation to the input signal.
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Deep Learning Based Automatic Modulation Recognition: Models, Datasets, and Challenges
An Efficient Deep Learning Model for Automatic Modulation Recognition Based on Parameter Estimation and Transformation
A light neural network for modulation detection under impairments
STF-GCN: A Multi-Domain Graph Convolution Network Method for Automatic Modulation Recognition via Adaptive Correlation
Ultralight Signal Classification Model for Automatic Modulation Recognition
Parameter Estimation based Automatic Modulation Recognition for Radio Frequency Signal
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