Precoder Design in Multi-User FDD Systems with VQ-VAE and GNN

ABSTRACT Robust precoding is efficiently feasible in frequency divis ion duplex (FDD) systems by incorporating the learnt statistic s of the propagation environment through a generative model. W e 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 au toen-coder (VQ-V AE), 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 lear n-ing architecture of the VQ-V AE allows us to jointly train the GNN together with VQ-V AE 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 pr o-posed frameworks over the conventional methods involving the sub-discrete Fourier transform (DFT) pilot matrix and i t-erative precoder algorithms enabling the deployment of sys - tems characterized by fewer pilots or feedback bits.