Traditional wireless communication theory is based on complex probabilistic models and fixed conjectures, which limit the optimal utilization of spectrum resources. Deep learning has been used to design end-to-end communication systems using an encoder to replace the transmitter and a decoder for the receiver. We address the challenge to update the parameters of a wireless channel autoencoder (AE) under a time-varying channel with short coherence-time. We suggest an optimized training algorithm that updates the learning rate value on a per-dimension basis, restricting the past gradients instead of accumulating them. We also scale the initial weights of our AE by sampling them from a normalized uniform distribution. While recently proposed AE configurations might fail to converge at a few number of epochs, our setting attains a fast convergence maintaining its robustness to large gradients, oscillations, and vanishing problems. By simulation results, we demonstrate that our proposed AE configuration improves the bit reconstruction accuracy in shorter training time.

中文翻译：

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为短相干时间通信加速无线信道自动编码器

传统的无线通信理论基于复杂的概率模型和固定的猜想，限制了频谱资源的优化利用。深度学习已被用于设计端到端通信系统，使用编码器代替发送器和接收器的解码器。我们解决了在具有短相干时间的时变信道下更新无线信道自动编码器 (AE) 参数的挑战。我们建议一种优化的训练算法，它在每个维度的基础上更新学习率值，限制过去的梯度而不是累积它们。我们还通过从归一化均匀分布中采样来缩放 AE 的初始权重。虽然最近提出的 AE 配置可能无法在几个时期内收敛，我们的设置实现了快速收敛，同时保持了对大梯度、振荡和消失问题的鲁棒性。通过仿真结果，我们证明了我们提出的 AE 配置在更短的训练时间内提高了位重建精度。