Pilot Contamination Aware Transformer for Downlink Power Control in Cell-Free Massive MIMO 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. Base station (BS) coordination eliminates inter-cell interference and allows multi-user massive multiple-input multiple-output (MIMO) to serve users distributed over a large geographic area. An initial part of this work was presented at 56th Asilomar Conference on Signals Systems, and Computers, Asilomar, CA, USA, Nov. 2022. A. K. Kocharlakota and S. A. V orobyov are with the Department of Information and Communications Engineering, Aalto University, PO Box 15400, 00076 Aalto, Finland. R. W . Heath Jr. is with the Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gillman Dr, La Jolla, CA, US 92093. This material is based upon work supported in part by the National Science Foundation under Grant No. NSF-CCF-2435254. To fully leverage the benefits of BS coordination, sophisticated pilot allocation and power control algorithms are essential. These algorithms face significant computational complexities due to the centralized signal processing tasks [9-11].