Transformer-Based Power Optimization for Max-Min Fairness in Cell-Free Massive MIMO
This work addresses power optimization for max-min fairness in wireless communication networks, offering a flexible solution for dynamic scenarios, though it is incremental as it applies transformers to a known bottleneck.
The paper tackled the power allocation problem in dynamic cell-free massive MIMO networks by proposing a transformer-based model that predicts optimal uplink and downlink power using user and access point positions, achieving near-optimal performance and adapting to varying network conditions without retraining.
Power allocation is an important task in wireless communication networks. Classical optimization algorithms and deep learning methods, while effective in small and static scenarios, become either computationally demanding or unsuitable for large and dynamic networks with varying user loads. This letter explores the potential of transformer-based deep learning models to address these challenges. We propose a transformer neural network to jointly predict optimal uplink and downlink power using only user and access point positions. The max-min fairness problem in cell-free massive multiple input multiple output systems is considered. Numerical results show that the trained model provides near-optimal performance and adapts to varying numbers of users and access points without retraining, additional processing, or updating its neural network architecture. This demonstrates the effectiveness of the proposed model in achieving robust and flexible power allocation for dynamic networks.