This paper presents a study on the application of convolutional neural networks (CNNs) to the complex-valued temporal radio signal domain for modulation recognition. The authors compare the effectiveness of using naively learned features with expert feature-based methods, showing that CNNs provide significant performance improvements, especially at low signal-to-noise ratios (SNR). They demonstrate that deep convolutional neural networks can effectively learn features from large, densely encoded time series data, making them a strong candidate for modulation recognition tasks. Radio communications present unique challenges for machine learning, as expert features and decision criteria have been traditionally used. However, recent trends in image and voice recognition suggest a shift towards feature learning from data rather than crafting expert features. The authors propose using CNNs and deep neural networks (DNNs) to learn features across a wide range of tasks, offering flexibility and improved classification accuracy. The paper evaluates a dataset of 11 modulation types, including 8 digital and 3 analog modulations, with a focus on their performance under varying SNR conditions. The dataset is generated using synthetic data with realistic channel effects, including multi-path fading, oscillator drift, and additive Gaussian white noise. The authors train several classifiers, including decision trees, K-nearest neighbors, Gaussian Naive Bayes, and SVMs, as well as CNNs and DNNs, to evaluate their performance. The results show that CNNs, particularly CNN2 with dropout regularization, achieve significantly better performance at low SNR compared to expert feature-based methods. At +18dB SNR, the CNN2 model achieves a clean diagonal in the confusion matrix, indicating high accuracy. The CNN2 model also shows improved performance at lower SNR levels, outperforming expert feature-based systems by up to 5dB. The authors conclude that CNNs are a viable and effective approach for modulation recognition in radio communications, especially in low SNR environments. They suggest that CNNs could be a strong candidate for dynamic spectrum access (DSA) and cognitive radio (CR) systems, as they offer robust classification of radio emitters even under challenging conditions. The study highlights the potential of CNNs to improve the performance of radio communication systems through feature learning and adaptive classification.This paper presents a study on the application of convolutional neural networks (CNNs) to the complex-valued temporal radio signal domain for modulation recognition. The authors compare the effectiveness of using naively learned features with expert feature-based methods, showing that CNNs provide significant performance improvements, especially at low signal-to-noise ratios (SNR). They demonstrate that deep convolutional neural networks can effectively learn features from large, densely encoded time series data, making them a strong candidate for modulation recognition tasks. Radio communications present unique challenges for machine learning, as expert features and decision criteria have been traditionally used. However, recent trends in image and voice recognition suggest a shift towards feature learning from data rather than crafting expert features. The authors propose using CNNs and deep neural networks (DNNs) to learn features across a wide range of tasks, offering flexibility and improved classification accuracy. The paper evaluates a dataset of 11 modulation types, including 8 digital and 3 analog modulations, with a focus on their performance under varying SNR conditions. The dataset is generated using synthetic data with realistic channel effects, including multi-path fading, oscillator drift, and additive Gaussian white noise. The authors train several classifiers, including decision trees, K-nearest neighbors, Gaussian Naive Bayes, and SVMs, as well as CNNs and DNNs, to evaluate their performance. The results show that CNNs, particularly CNN2 with dropout regularization, achieve significantly better performance at low SNR compared to expert feature-based methods. At +18dB SNR, the CNN2 model achieves a clean diagonal in the confusion matrix, indicating high accuracy. The CNN2 model also shows improved performance at lower SNR levels, outperforming expert feature-based systems by up to 5dB. The authors conclude that CNNs are a viable and effective approach for modulation recognition in radio communications, especially in low SNR environments. They suggest that CNNs could be a strong candidate for dynamic spectrum access (DSA) and cognitive radio (CR) systems, as they offer robust classification of radio emitters even under challenging conditions. The study highlights the potential of CNNs to improve the performance of radio communication systems through feature learning and adaptive classification.