用自编码器设计无信道状态信息的短码，性能优于传统码。
Learning Short Codes for Fading Channels with No or Receiver-Only Channel State Information
- 用自编码器联合学习编码与解码，适配无CSI和仅接收端有CSI场景。
- 在无CSI下，当信道衰落支持全实数域时，码字相互正交。
- 相比针对高斯信道设计的码，专为衰落信道设计的码性能更优。
在下一代无线网络中，低延迟常要求使用短码长的码字，且不依赖或仅依赖接收端的信道状态信息（CSIR）。高斯码虽可实现高斯白噪声（AWGN）信道容量，但在无信道状态信息（no-CSI）和仅接收端有信道状态信息（CSIR-only）场景下可能不适用。本文采用自编码器架构设计适用于这些场景的短码。实验发现：在无CSI情况下，当信道衰落随机变量的实部与虚部支持在整个实数线上时，学习到的码字相互正交；而当支持限于非负实数线时，则不正交。在仅接收端有信道状态信息的情况下，针对AWGN信道设计的深度学习码在衰落信道中，以最优相干检测性能劣于专为衰落信道与CSIR设计的码，后者通过自编码器联合学习编码、相干合并与解码。在无CSI和仅接收端有信道状态信息两种情况下，所设计码的性能均不低于或优于同块长的传统码。
原文摘要 · Abstract (English)
In next-generation wireless networks, low latency often necessitates short-length codewords that either do not use channel state information (CSI) or rely solely on CSI at the receiver (CSIR). Gaussian codes that achieve capacity for AWGN channels may be unsuitable for these no-CSI and CSIR-only cases. In this work, we design short-length codewords for these cases using an autoencoder architecture. From the designed codes, we observe the following: In the no-CSI case, the learned codes are mutually orthogonal when the distribution of the real and imaginary parts of the fading random variable has support over the entire real line. However, when the support is limited to the non-negative real line, the codes are not mutually orthogonal. For the CSIR-only case, deep learning-based codes designed for AWGN channels perform worse in fading channels with optimal coherent detection compared to codes specifically designed for fading channels with CSIR, where the autoencoder jointly learns encoding, coherent combining, and decoding. In both no-CSI and CSIR-only cases, the codes perform at least as well as or better than classical codes of the same block length.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。