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On deep learning-based communication over the air

机译：关于基于深度学习的空中交流

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In this work, we demonstrate an over-the-air communications system which is solely based on deep neural networks and has, thus far, only been validated by computer simulations for block-based transmissions. Transmitter and receiver can be jointly trained end-to-end for an arbitrary differentiable end-to-end performance metric, e.g., block error rate (BLER). We demonstrate that it is possible to build and train such a system using off-the-shelf software-defined radios (SDRs) and open-source deep learning software libraries. A comparison of the BLER performance of the "learned" system against that of a practical baseline shows competitive performance. We identify several practical challenges of training such a system over-the-air, in particular the missing channel gradient, and propose a learning procedure that circumvents this issue.
机译：在这项工作中，我们演示了仅基于深度神经网络的空中通信系统，到目前为止，仅通过计算机模拟对基于块的传输进行了验证。可以针对任意可区分的端到端性能指标（例如，块错误率（BLER））对端到端联合培训发送器和接收器。我们证明，可以使用现成的软件定义无线电（SDR）和开源深度学习软件库来构建和训练这样的系统。将“学习的”系统的BLER性能与实际基准的BLER性能进行比较，可以得出竞争性能。我们确定了通过空中培训这种系统的一些实际挑战，特别是缺少的信道梯度，并提出了一种规避此问题的学习程序。

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