Introduction - If you have any usage issues, please Google them yourself
End-to-end learning of communications systems is a
fascinating novel concept that has so far only been validated by
simulations for block-based transmissions. It allows learning of
transmitter and receiver implementations as deep neural networks
(NNs) that are optimized for an arbitrary differentiable end-to-end
performancemetric, block error rate (BLER). In this paper, we
demonstrate that over-the-air transmissions are possible:We build,
train, and run a complete communications system solely composed
of NNs using unsynchronized off-the-shelf software-defined radios
and open-source deep learning software libraries.