Using ML to create unique descriptors?
If I understood correctly your question, you want a function that takes a signal (a fixed window) and outputs a 32-bit representation in a way that the correlation between it and any other signal is preserved. Mathematically speaking given signals $s_1$ and $s_2$ in $S$ and a correlation function $corr(s_1, s_2)$ you want some function $f : S \rightarrow B$ (where $B$ is the space of 32-bit binary numbers) that you could use another correlation function for instance $corr_f(b_1, b_2) \approx corr(s_1, s_2)$.

If that is what you want you should at hashing techniques in particular learning to hash. Essentially in hashing want you do is you represent your input by binary numbers in a way that the hamming distance between the binary numbers preserves some target similarity (distance) function such as the correlation. In particular for cross-correlation (inner product) you have some methods based on random projections.

So once you have learned (designed) your hashing function $f$, what I would do is:

b1 = f(s1)
send b1
receive b1
b2 = f(s2)
return h(b2, b1) # this value is going to tell you if the signals are correlated