How is deep learning applied in telecommunications?
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I work in the field of telecommunications. My goal is to plot BER values against certain SNR values. I send a certain number of bits to MATLAB and determine how many of them are true and how many are false. For example, 0 dB -> 0.1668; 5 dB -> 0.0518; 0.0101.
The code I wrote in MATLAB works for SNR in decibels, I am trying to find the BER value for each SNR value. In addition to classical communication, I want to do these operations with Deep Learning in MATLAB.
For example, it will be very good for my work to do the following:
1) For example, I want to have the following Maximum-Likelihood process done by Deep learning, but I don't know how to do it.
Metrics(loop,loop_h) = norm(y-H(:,loop_h)*ss(1,loop))^2; %Here, "ss" stands for M-QAM modulation symbols and H stands for Rayleigh fading channel
2) I would like to have channel estimation if possible. For example, I express the Rayleigh channel as follows. How can I predict this with Deep learning in MATLAB?
H=sqrt(0.5).*(randn(nR,nT) + 1i*randn(nR,nT));
Any suggestions on how I can do these operations, a book, a piece of code, etc.? I would appreciate it if you could share it with me. Thanks in advance for your suggestions and help.