We consider a trainable point-to-point communication system, where both
transmitter and receiver are implemented as neural networks (NNs), and
demonstrate that training on the bit-wise mutual information (BMI) allows
seamless integration with practical bit-metric decoding (BMD) receivers, as
well as joint optimization of constellation shaping and labeling. Moreover, we
present a fully differentiable neural iterative demapping and decoding (IDD)
structure which achieves significant gains on additive white Gaussian noise
(AWGN) channels using a standard 802.11n low-density parity-check (LDPC) code.
The strength of this approach is that it can be applied to arbitrary channels
without any modifications. Going one step further, we show that careful code
design can lead to further performance improvements. Lastly, we show the
viability of the proposed system through implementation on software-defined
radios (SDRs) and training of the end-to-end system on the actual wireless
channel. Experimental results reveal that the proposed method enables
significant gains compared to conventional techniques.

该研究提出了一种可训练的点对点通信系统，其中发射机和接收机均实现为神经网络，通过比特互信息训练并联结实用的比特度量译码接收机，同时联合优化星座图造型和标注，通过完全可微分的神经网络迭代解调和解码结构，在不修改任何条件的情况下处理任意通道，采用软件定义无线电实现并训练了端到端系统，实验结果表明，与传统技术相比，该方法具有显著的性能优势。