There is plenty of research on ML in signal processing. The majority of it, so it seams to me, is about showing feasibility of ML-based receivers (end-to-end or individual functional blocks of). To me, we are past that – pretty much everybody realizes that ML-based OFDM receiver is possible and can probably achieve comparable performance to that of a conventional receiver. Furthermore, even if/when someone manages to show some (probably marginal) performance gain, that would probably have academic value, but not much beyond that.

To me, there are two fundamental questions, for which I haven’t found answers in the literature and would really appreciate some pointers:

1. Can ML-based receiver achieve comparable performance with comparable complexity (to these of a conventional receiver)?
2. Beyond (probably marginal) performance gains, what can be commercial benefits of switching from a conventional receiver to an ML-based one?

Thanks!