Classic Signal Processing vs Deep Learning / Machine Learning (DNN / ML) Based Signal Processing
This is an open discussion.
In my opinion, any field we couldn't figure near optimal solution (And in most cases we only have Linear Optimal solution) using Deep Learning will replace classic methods given enough data to work with.
The power of data driven features vs. intuitive (Though mostly right yet never cover everything) will prevail.
I don't see it as a problem, Deep Learning isn't throw it all and it work machine, it still requires domain knowledge to effectively build the training process, the architecture and mostly the model to optimize. Which, if out think about it, just like in a classic method: Build test case, build a model, build a solver.
So one can see it as an evolutionary step where the optimization and the model are greatly improved yet the method has the same rational.
I believe near the hardware Signal Processing will stay as it is for longer time (Decimation, Filtering, etc...). Though in image processing DL is doing steps towards this as well. But in RF I think it will stay longer. But the next steps (For instance, Matched Filter) can be replaced (In Low SNR?) by DL.