Pilot Contamination Aware Transformer for Downlink Power Control in Cell-Free Massive MIMO Networks
Atchutaram K. Kocharlakota, Sergiy A. Vorobyov, Robert W. Heath, Jr

TL;DR
This paper proposes a pilot contamination-aware transformer neural network for downlink power control in cell-free massive MIMO systems, improving efficiency and scalability while effectively handling pilot contamination.
Contribution
It introduces a novel transformer-based neural network that incorporates pilot allocation data and attention mechanisms for improved power control in large-scale CFmMIMO networks.
Findings
PAPC achieves comparable spectral efficiency to traditional algorithms.
PAPC significantly reduces computational complexity.
The model scales well with increasing network size.
Abstract
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…
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Taxonomy
TopicsAdvanced MIMO Systems Optimization · Cooperative Communication and Network Coding · Energy Harvesting in Wireless Networks
MethodsSoftmax · Attention Is All You Need
