Distributed Joint Design of Fairness Scheduling and Beamforming in User-Dense Cell-Free Networks

In scenarios where the number of users far exceeds the number of base station antennas, it becomes infeasible to serve all users simultaneously. We observe that scheduling more users at first steadily improves system performance, but this improvement eventually reaches saturation. Moreover, scheduling excessive users per time slot results in diminishing gains per user and a significant increase in computational complexity. Consequently, only a subset of users can be selected for service in each time slot. Given that the fairness scheduling and precoding problems are coupled, we employ learning-based methods to rapidly schedule users, reducing the complexity of subsequent joint optimization problems. To facilitate effective backpropagation despite discrete scheduling actions, we design the network's output layer accordingly and introduce a layer-wise pruning mechanism to enhance learning accuracy and speed. By leveraging the interference relationships, we partition the base station into clusters, thereby transforming the centralized joint optimization problem into multiple sub-problems, which can be tackled in a distributed manner.