具有条件GAN的基于信道无关端到端学习的通信系统
Channel Agnostic End-to-End Learning based Communication Systems with Conditional GAN
摘要：
在本文中，我们使用深度神经网络（DNN）来开发无线端到端通信系统，其中DNN用于所有与信号相关的功能，例如编码，解码，调制和均衡。然而，需要精确的瞬时信道传递函数\emph{i.e.}（信道状态信息（CSI））来计算DNN表示的梯度。在许多通信系统中，信道传递函数很难预先获得，并且随时间和位置而变化。在本文中，通过开发一个与通道无关的端到端系统来释放这一约束，该系统不依赖于关于通道的任何先验信息。我们使用条件生成对抗网络（GAN）来表示信道效应，其中发送器的编码信号将用作条件信息。另外，为了处理时变信道，也可以将与导频数据对应的接收信号作为条件信息的一部分添加。仿真结果表明，该方法在加性高斯白噪声（AWGN）和瑞利衰落信道下都是有效的，为构建数据驱动的通信系统打开了一扇新的大门。
英文摘要：
In this article, we use deep neural networks (DNNs) to develop a wireless end-to-end communication system, in which DNNs are employed for all signal-related functionalities, such as encoding, decoding, modulation, and equalization. However, accurate instantaneous channel transfer function, \emph{i.e.}, the channel state information (CSI), is necessary to compute the gradient of the DNN representing. In many communication systems, the channel transfer function is hard to obtain in advance and varies with time and location. In this article, this constraint is released by developing a channel agnostic end-to-end system that does not rely on any prior information about the channel. We use a conditional generative adversarial net (GAN) to represent the channel effects, where the encoded signal of the transmitter will serve as the conditioning information. In addition, in order to deal with the time-varying channel, the received signal corresponding to the pilot data can also be added as a part of the conditioning information. From the simulation results, the proposed method is effective on additive white Gaussian noise (AWGN) and Rayleigh fading channels, which opens a new door for building data-driven communication systems.
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。
