Pilot Contamination-Aware Graph Attention Network for Power Control in CFmMIMO
This addresses real-time power control challenges in wireless communication systems, but it is incremental as it builds on existing GNN approaches with specific improvements.
The paper tackles the problem of high computational complexity and unrealistic assumptions in power control for cell-free massive MIMO systems by proposing a self-supervised graph attention network that handles pilot contamination and dynamic user numbers, showing effectiveness compared to an optimal baseline method.
Optimization-based power control algorithms are predominantly iterative with high computational complexity, making them impractical for real-time applications in cell-free massive multiple-input multiple-output (CFmMIMO) systems. Learning-based methods have emerged as a promising alternative, and among them, graph neural networks (GNNs) have demonstrated their excellent performance in solving power control problems. However, all existing GNN-based approaches assume ideal orthogonality among pilot sequences for user equipments (UEs), which is unrealistic given that the number of UEs exceeds the available orthogonal pilot sequences in CFmMIMO schemes. Moreover, most learning-based methods assume a fixed number of UEs, whereas the number of active UEs varies over time in practice. Additionally, supervised training necessitates costly computational resources for computing the target power control solutions for a large volume of training samples. To address these issues, we propose a graph attention network for downlink power control in CFmMIMO systems that operates in a self-supervised manner while effectively handling pilot contamination and adapting to a dynamic number of UEs. Experimental results show its effectiveness, even in comparison to the optimal accelerated projected gradient method as a baseline.