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LEARNINGBASED AUTOMATIC MODULATION CLASSIFICATION by Amen Eliding AbdelmutalabA Thesis Presented to the Faculty of the American University of Shariah College of Engineering in Partial Fulfillment of

How to fill out learning-based automatic modulation classification

How to fill out learning-based automatic modulation classification

1. Step 1: Gather labeled dataset of modulated signal samples.
2. Step 2: Preprocess the data by applying suitable techniques such as normalization and noise reduction.
3. Step 3: Split the dataset into training and testing sets.
4. Step 4: Select an appropriate machine learning algorithm for classification, such as support vector machines (SVM) or convolutional neural networks (CNN).
5. Step 5: Train the chosen algorithm using the training dataset.
6. Step 6: Evaluate the trained model using the testing dataset to assess its performance.
7. Step 7: Fine-tune the model by adjusting hyperparameters or trying different algorithms to improve classification accuracy.
8. Step 8: Once satisfied with the model's performance, use it to classify new modulated signals by feeding the signal features as input and obtaining the predicted modulation type as the output.

Who needs learning-based automatic modulation classification?

1. Learning-based automatic modulation classification is needed by various industries and applications such as:
2. Telecommunication companies: It helps in automatic detection and classification of modulated signals, which aids in efficient signal processing and optimization of communication systems.
3. Military and defense organizations: It plays a crucial role in detecting and identifying different types of modulated signals used in military communications and radar systems.
4. Wireless sensor networks: Classification of modulated signals can be useful for monitoring and controlling wireless sensor networks.
5. Signal intelligence organizations: It helps in analyzing and understanding the characteristics of different modulated signals for intelligence and surveillance purposes.
6. Researchers and academics: Learning-based automatic modulation classification provides a valuable tool for studying and analyzing various modulation schemes and their performance.

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What is learning-based automatic modulation classification?

Learning-based automatic modulation classification is a process of using machine learning algorithms to identify and categorize the modulation scheme used in a signal.

Who is required to file learning-based automatic modulation classification?

Entities or organizations involved in wireless communications or signal processing may be required to file learning-based automatic modulation classification.

How to fill out learning-based automatic modulation classification?

To fill out learning-based automatic modulation classification, data samples of modulated signals are collected and analyzed using machine learning models.

What is the purpose of learning-based automatic modulation classification?

The purpose of learning-based automatic modulation classification is to automatically classify and identify the modulation scheme used in a signal for various communication or signal processing applications.

What information must be reported on learning-based automatic modulation classification?

Information such as the type of modulation scheme, probability distribution, signal-to-noise ratio, and any relevant features extracted from the signal must be reported on learning-based automatic modulation classification.