There is many decades on research on MIMO detection: https://arxiv.org/abs/1507.05138

The most competitive methods are non-linear and crafted to exploit the structure of the problem. There are also more recent deep learning methods but they appear to not be so competitive.

Whether deep learning can be used in this detectors in massive MIMO

please help me ]]>

Aha, I suppose you are referring to MIMO equalization/decoding/detection. I recommend you to have a look at the recent works on MIMO detection using learning. Here is one example: https://arxiv.org/abs/2002.02750

]]>I meant to decorrelate the received signal before decoding. Thanks for your answer.

]]>I’m not sure what this means. I don’t think that deep learning can change the channel itself, but it can be used to design algorithms. The question is whether it can do better than existing algorithms.

]]>I think that it could, assuming only that the level of correlation is stable. Maybe a parameter such as condition number would be useful for training. ]]>

I was also expecting that before I started, but I think there are two reasons why that didn’t happen: 1) The training data is “easily” classified using separating lines, 2) The input to every neuron in a hidden layer is an inner product plus a scalar bias, which can be viewed as the line equation. Hence, we are basically measuring if the input vector is a point above or below a particular line, and in this case, we just had a learn some simple lines to compare against.

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