Pilot Contamination Aware Transformer for Downlink Power Control in Cell-Free Massive MIMO Networks
This work addresses computational bottlenecks in wireless communication systems, offering a more efficient solution for network operators, though it is incremental as it builds on existing learning-based methods by incorporating pilot information.
The paper tackled the problem of computationally intensive downlink power control in cell-free massive MIMO networks by introducing a pilot contamination-aware transformer neural network, which achieved comparable spectral efficiency fairness to an iterative optimization algorithm while significantly improving computational efficiency and scalability to large-scale networks.
Learning-based downlink power control in cell-free massive multiple-input multiple-output (CFmMIMO) systems offers a promising alternative to conventional iterative optimization algorithms, which are computationally intensive due to online iterative steps. Existing learning-based methods, however, often fail to exploit the intrinsic structure of channel data and neglect pilot allocation information, leading to suboptimal performance, especially in large-scale networks with many users. This paper introduces the pilot contamination-aware power control (PAPC) transformer neural network, a novel approach that integrates pilot allocation data into the network, effectively handling pilot contamination scenarios. PAPC employs the attention mechanism with a custom masking technique to utilize structural information and pilot data. The architecture includes tailored preprocessing and post-processing stages for efficient feature extraction and adherence to power constraints. Trained in an unsupervised learning framework, PAPC is evaluated against the accelerated proximal gradient (APG) algorithm, showing comparable spectral efficiency fairness performance while significantly improving computational efficiency. Simulations demonstrate PAPC's superior performance over fully connected networks (FCNs) that lack pilot information, its scalability to large-scale CFmMIMO networks, and its computational efficiency improvement over APG. Additionally, by employing padding techniques, PAPC adapts to the dynamically varying number of users without retraining.