Hello
i need an advice with channel equalizer based on neural network. i have data that represent the received signals at different channel conditions and the target output and the channel response without the signal and i want the loss function minimize the error between the target and received at each condition. the problem is that i don't know how to train my network what is the input. i'm using MLP type which is similar to feed forward network i'll appreciate if anyone can help
Mariam Qutiaba
To train your neural network for channel equalization, you can use the received signals as input and the target output as output. The goal of the network is to learn the channel response and use this information to equalize the received signal so that it matches the target output.
Assuming that you have multiple sets of received signals and corresponding target outputs for different channel conditions, you can use these as training data for your network. In other words, you can train your network to map the received signal to the target output for each channel condition.
To set up your network, you can use an MLP architecture that takes the received signal as input and outputs the equalized signal. The number of neurons in the input layer should be equal to the number of features in your received signal, and the number of neurons in the output layer should be equal to the number of samples in your target output. You can experiment with the number of hidden layers and the number of neurons in each hidden layer to see what works best for your problem.
For the loss function, you can use a mean squared error (MSE) loss between the network output and the target output. The MSE loss is a common choice for regression problems and will penalize the network for large errors between the output and the target.
Once you have set up your network architecture and loss function, you can use standard backpropagation techniques to train the network. During training, the network will learn to adjust its weights to minimize the MSE loss between the output and the target.
It's worth noting that channel equalization is a challenging problem, and the performance of your network will depend on the complexity of the channel response and the quality of your training data. It's important to carefully preprocess your data and experiment with different network architectures and training parameters to get the best results.
Mariam Qutiaba
When using a neural network for channel equalization, the input to the network should be the received signal at each channel condition, and the target output should be the original transmitted signal. The channel response should be used to simulate the channel distortion and generate the received signals for each condition.
Here are the steps you can follow to train your MLP neural network for channel equalization:
thank you all for these helpful answers and clarification
last question Abderrahmane Boudribila can i train my network to selected points of the signal since each signal vector has like 700000 points making the training time large
and what about complex values (magnitude and phase) should i take the real part separate from the imaginary
Mariam Qutiaba
Yes, you can train your neural network to selected points of the signal if you want to reduce the training time. One way to do this is to use a sliding window approach where you select a fixed number of contiguous points from the received signal and use them as the input to the neural network. You can slide the window along the signal and extract multiple inputs and corresponding target outputs. This approach can help reduce the input size and training time.
Mariam Qutiaba
Regarding the use of complex values, you can represent the complex valued received signal as a two-dimensional matrix, where the real part is the first dimension and the imaginary part is the second dimension. You can then use this two-dimensional matrix as the input to your neural network.
Mariam Qutiaba
If you want to further separate the magnitude and phase information, you can convert the complex valued signal to polar coordinates. The magnitude and phase information can be represented as two separate one-dimensional signals, which can be used as separate inputs to your neural network. However, keep in mind that the magnitude and phase information are interdependent and encoding them separately may not be the most optimal representation for your specific application.
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