论文标题
通过深度学习和LDPC代码，一位量化的通道上的高率通信
High Rate Communication over One-Bit Quantized Channels via Deep Learning and LDPC Codes
论文作者
论文摘要
本文提出了一种通过将已知编码方案与自动编码器相结合的方法来设计错误校正代码的方法。具体而言，我们将LDPC代码与训练有素的自动编码器集成在一起，以开发用于棘手的非线性通道的错误校正代码。 LDPC编码器缩小了自动编码器的输入空间，这使自动编码器能够更轻松地学习。提出的误差校正代码显示了一位量化的有希望的结果，这是非线性通道的挑战性案例。具体而言，即使具有一位量化，我们的设计即使使用高阶调制格式（例如16-QAM和64-QAM），我们的设计也给出了瀑布坡度错误率。从理论上讲，通过证明训练有素的自动编码器向LDPC解码器提供大约高斯分布数据，即使接收的信号由于一位量化而具有非高斯统计数据，因此这一增益是基于基础的。
This paper proposes a method for designing error correction codes by combining a known coding scheme with an autoencoder. Specifically, we integrate an LDPC code with a trained autoencoder to develop an error correction code for intractable nonlinear channels. The LDPC encoder shrinks the input space of the autoencoder, which enables the autoencoder to learn more easily. The proposed error correction code shows promising results for one-bit quantization, a challenging case of a nonlinear channel. Specifically, our design gives a waterfall slope bit error rate even with high order modulation formats such as 16-QAM and 64-QAM despite one-bit quantization. This gain is theoretically grounded by proving that the trained autoencoder provides approximately Gaussian distributed data to the LDPC decoder even though the received signal has non-Gaussian statistics due to the one-bit quantization.
