通信系统的端到端学习是a
引人入胜的新颖概念迄今为止仅被验证
模拟基于块的传输。它允许学习
发射机和接收机实现为深度神经网络
（NN），它们针对任意可区分的端到端进行了优化
performancemetric，例如块错误率（BLER）。在本文中，我们
证明无线传输是可能的：我们建造，
训练，并运行完整的通讯系统
的神经网络使用非同步的现成软件定义无线电
和开源深度学习软件库。(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.)