Scalable Pilot, Feedback and Precoding Design for Fdd Multi-User MIMO via Deep Learning

This paper considers the joint pilot, feedback, and precoding design for low-overhead downlink multi-user multiple-input multiple-output (MU-MIMO) communication in frequency-division duplex (FDD) systems. Although the deep learning (DL) technique has demonstrated the potential to address this challenging problem, most of the current works along this line suffer from poor scalability because the numbers of users during the training and implementation phases have to be the same. To overcome this limitation, we propose a novel scalable DL framework for joint transceiver design in FDD systems using neural networks (NNs). Specifically, we design a vector-quantized variational autoencoder-based feedback scheme with a common quantization codebook shared by all the users for efficient feedback. Additionally, we employ a graph attention network (GAT) for effective precoding design. Thanks to the shared feedback module and the inherent scalability of GAT, the proposed NN trained for a particular number of users can be applied in practice given any number of users with satisfactory performance. Numerical results using a real-world channel dataset are provided to demonstrate the superior scalability and performance of the proposed approach over the existing baseline methods.