English
资源说明：Abstract—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, e.g., 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.We extend the
existing ideas toward continuous data transmission, which eases
their current restriction to short block lengths but also entails the
issue of receiver synchronization. We overcome this problem by
introducing a frame synchronizationmodule based on another NN.
A comparison of the BLER performance of the “learned” system
with that of a practical baseline shows competitive performance
close to 1 dB, even without extensive hyperparameter tuning. We
identify several practical challenges of training such a system over
actual channels, in particular, the missing channel gradient, and
propose a two-step learning procedure based on the idea of transfer
learning that circumvents this issue.
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