Joint Access Point Selection and Precoder Design under Statistical CSI

This work addresses joint access point (AP) selection and precoding for sum-rate maximization under statistical channel state information (CSI) in multi-AP multi-user systems. To this end, we propose two approaches. The first method is an iterative alternating optimization algorithm that updates the precoding vectors via the stochastic WMMSE (SWMMSE) algorithm and the assignment variables via a projected gradient descent step. The second method is a graph neural network (GNN)-based framework that solves the same problem in a single forward pass during inference. Building on an attention-based Edge-GNN architecture, we extend it to a multi-AP scenario, enabling the joint learning of assignment variables and precoding vectors from statistical CSI alone. Results show that the GNN outperforms the iterative algorithm across the tested signal-to-noise ratio (SNR) range and generalizes to varying numbers of users with comparable performance. Both approaches are also compared to various baseline techniques.