- Changing the hyper parameters of the model layers.
- Increasing the size of data input.
- Try increasing number of layers and number of neurons in the layers.

# Tracking channel states using Machine Learning

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I am new in AI and would like to apply machine learning to estimate the channel states. I have a set of data. It is a matrix of 10000*8. Each row of this matrix is regarding a time step, i.e., 1st row = current time step (t), 2nd row = next time step (t+1), and so on. Each column is related to one transmitter, and I have 8 different transmitters. Each transmitter at each time step estimates the channel status and assigns one value form the set {0,1,2,-1}. for example the n-th row has a form of [ -1 0 0 0 1 0 0 2].

Knowing the status of the channel in the time step t I would like to know what is the channel states in time step (t+1).

I used the MLP and got the mse = 0.04 but the activate function that I used is either 'tansig' or 'logsig'. the results then is in [0, 1] or [-1, 1] and I don't know how to convert them to {-1, 0, 1, 2}. I have also applied LSTM but the mse error is 0.31. So I don't know why I get such a large mse. Any suggestions would be greatly appreciated.

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### Answers (1)

Vimal Rathod
on 25 Jul 2019

The Multi-Layer Perceptron Algorithm is generally used for Regression and the use case you are working on is Classifying and predicting the channel state of the future. Thus, for such time series classification LSTM networks are a better option. Coming to the getting a high MSE value it can be reduced by using the following methods:

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