Tree Meets Transformer: A Hybrid Architecture for Scalable Power Allocation in Cell-Free Networks
This addresses power allocation in cell-free networks for wireless communication, offering a scalable solution for dynamic user loads, though it is incremental as it builds on existing Transformer methods.
The paper tackles the challenge of scalable power allocation in wireless networks by proposing a hybrid Tree-Transformer architecture, achieving near-optimal performance with logarithmic depth and linear complexity, reducing inference time compared to baselines.
Power allocation remains a fundamental challenge in wireless communication networks, particularly under dynamic user loads and large-scale deployments. While Transformerbased models have demonstrated strong performance, their computational cost scales poorly with the number of users. In this work, we propose a novel hybrid Tree-Transformer architecture that achieves scalable per-user power allocation. Our model compresses user features via a binary tree into a global root representation, applies a Transformer encoder solely to this root, and decodes per-user uplink and downlink powers through a shared decoder. This design achieves logarithmic depth and linear total complexity, enabling efficient inference across large and variable user sets without retraining or architectural changes. We evaluate our model on the max-min fairness problem in cellfree massive MIMO systems and demonstrate that it achieves near-optimal performance while significantly reducing inference time compared to full-attention baselines.