Automatic Digital Modulation Detection Using Neural Networks in MATLAB | AI-Powered Signal Processin
Automatic digital modulation detection is a technique used to identify the type of modulation used in a digital signal. One way to accomplish this is through the use of neural networks.
A neural network is a machine learning algorithm that is modeled after the structure of the human brain. It is made up of layers of interconnected nodes, or "neurons," which process information. In the case of modulation detection, a neural network can be trained to recognize the characteristics of different types of modulation, such as amplitude modulation (AM) and frequency modulation (FM).
To train a neural network for modulation detection, a dataset of modulated signals must be collected. This dataset should include a variety of different modulation types and signal conditions. Once the dataset is collected, the neural network can be trained to recognize the patterns in the data that correspond to different modulation types.
Once the neural network is trained, it can be used to automatically detect the modulation type of a new signal. The neural network takes in the new signal as input and produces a output which indicates the modulation type. The output can be a probability distribution over the different modulation types or a single modulation type. The output is compared with the known modulation types and the one with the highest probability is considered as the modulation of the signal.
One of the advantages of using a neural network for modulation detection is that it can handle a wide range of signal conditions. The neural network can be trained to recognize modulation in signals that have been affected by noise, interference, or other distortions. This makes it well suited for use in real-world applications, such as wireless communication systems.
Another advantage of using a neural network for modulation detection is that it can be easily adapted to new modulation types. As new modulation schemes are developed, the neural network can be retrained to recognize them.
Problem Statement
Following points are considered as the problem statement;
- The automatic modulation detection is necessary for the correspondence system as permits the visually impaired identification of balance and lower the hardware cost of structuring the various demodulators at eceiver. The Noise in the medium additionally continues changing the conduct of the regulated sign. The automatic modulation classification makes the correspondence receiver adaptive.
- The automatic modulation detection work depends on different signal features which probably won't contribute towards classification accuracy and simply increment the overhead. To get the ideal arrangement of highlights which just add to precision, feature selection is an essential step.