用VQ-VAE改进多用户FDD系统预编码，减少反馈开销并提升速率
Precoder Design in Multi-User FDD Systems with VQ-VAE and GNN
- 结合VQ-VAE与GNN，端到端联合优化预编码和导频信号
- 在少导频或低反馈比特条件下，总速率显著优于传统DFT导频方法
- 适合资源受限的多用户无线系统部署，尤其关注反馈效率的场景
在频分双工（FDD）系统中，通过生成模型学习信道统计特性，可实现鲁棒的预编码。本文基于先前利用高斯混合模型（GMM）与图神经网络（GNN）设计基站专属预编码的工作，提出采用向量量化变分自编码器（VQ-VAE）克服GMM的关键缺陷：其成分数量随反馈比特数呈指数增长。此外，VQ-VAE的深度学习架构使我们能够将GNN与VQ-VAE及导频优化联合训练，构建端到端（E2E）模型，在多用户无线系统中显著提升总吞吐率。仿真表明，所提框架在使用较少导频或更低反馈比特时，性能明显优于传统基于子离散傅里叶变换（sub-DFT）导频矩阵和迭代预编码算法的方法。
原文摘要 · Abstract (English)
Robust precoding is efficiently feasible in frequency division duplex (FDD) systems by incorporating the learnt statistics of the propagation environment through a generative model. We build on previous work that successfully designed site-specific precoders based on a combination of Gaussian mixture models (GMMs) and graph neural networks (GNNs). In this paper, by utilizing a vector quantized-variational autoencoder (VQ-VAE), we circumvent one of the key drawbacks of GMMs, i.e., the number of GMM components scales exponentially to the feedback bits. In addition, the deep learning architecture of the VQ-VAE allows us to jointly train the GNN together with VQ-VAE along with pilot optimization forming an end-to-end (E2E) model, resulting in considerable performance gains in sum rate for multi-user wireless systems. Simulations demonstrate the superiority of the proposed frameworks over the conventional methods involving the sub-discrete Fourier transform (DFT) pilot matrix and iterative precoder algorithms enabling the deployment of systems characterized by fewer pilots or feedback bits.
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