使用递归神经网络的渐进传输
Progressive Transmission using Recurrent Neural Networks
摘要：
在本文中，我们研究了一种新的基于机器学习的传输策略，称为渐进传输或ProgTr。在ProgTr中，有b个变量应该使用最多T个通道使用来传输。发射机的目标是尽可能快地将数据发送到接收机，并且使用尽可能少的信道使用（在信道条件允许的情况下），而接收机在每次信道使用之后改进其估计。我们使用递归神经网络作为发射机和接收机的构建模块，其中SNR作为代表信道条件的输入。为了展示ProgTr的工作原理，在不同的场景下对所提出的方案进行了仿真，包括单/多用户设置、不同的信道条件以及离散和连续输入数据。结果表明，与传统的调制方法相比，ProgTr可以获得更好的性能。除了BER等性能指标外，还使用逐位互信息来解释发射机和接收机如何在ProgTr中工作。
英文摘要：
In this paper, we investigate a new machine learning-based transmission strategy called progressive transmission or ProgTr. In ProgTr, there are b variables that should be transmitted using at most T channel uses. The transmitter aims to send the data to the receiver as fast as possible and with as few channel uses as possible (as channel conditions permit) while the receiver refines its estimate after each channel use. We use recurrent neural networks as the building block of both the transmitter and receiver where the SNR is provided as an input that represents the channel conditions. To show how ProgTr works, the proposed scheme was simulated in different scenarios including single/multi-user settings, different channel conditions, and for both discrete and continuous input data. The results show that ProgTr can achieve better performance compared to conventional modulation methods. In addition to performance metrics such as BER, bit-wise mutual information is used to provide some interpretation to how the transmitter and receiver operate in ProgTr.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
