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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