Pilot Contamination-Aware Graph Attention Network for Power Control in CFmMIMO

• URL: http://arxiv.org/abs/2506.00967v2
• Date: Fri, 25 Jul 2025 12:42:35 GMT
• Title: Pilot Contamination-Aware Graph Attention Network for Power Control in CFmMIMO
• Authors: Tingting Zhang, Sergiy A. Vorobyov, David J. Love, Taejoon Kim, Kai Dong,
• Abstract summary: We propose a graph attention network for downlink power control in CFmMIMO systems.<n>It operates in a self-supervised manner while effectively handling pilot contamination and adapting to a dynamic number of user equipments.
• Score: 32.84946455950395
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning [52.64813150003228]
  The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring.<n>In the upcoming six-generation (6G) era, UAV-assisted mobile edge computing (MEC) is particularly crucial in challenging environments such as mountainous or disaster-stricken areas.<n>The task offloading problem is one of the key issues in UAV-assisted MEC, primarily addressing the trade-off between minimizing the task delay and the energy consumption of the UAV.
  arXiv  Detail & Related papers  (2025-01-11T02:32:42Z)
• Learning for Cross-Layer Resource Allocation in MEC-Aided Cell-Free Networks [71.30914500714262]
  Cross-layer resource allocation over mobile edge computing (MEC)-aided cell-free networks can sufficiently exploit the transmitting and computing resources to promote the data rate.<n>Joint subcarrier allocation and beamforming optimization are investigated for the MEC-aided cell-free network from the perspective of deep learning.
  arXiv  Detail & Related papers  (2024-12-21T10:18:55Z)
• Pilot Contamination Aware Transformer for Downlink Power Control in Cell-Free Massive MIMO Networks [45.487183737784086]
  This paper introduces the pilot contamination-aware power control (PAPC) transformer neural network.<n>PAPC integrates pilot allocation data into the network, effectively handling pilot contamination scenarios.<n>Trained in an unsupervised learning framework, PAPC is evaluated against the accelerated proximal gradient (APG) algorithm.
  arXiv  Detail & Related papers  (2024-11-28T09:48:52Z)
• Communication-Control Codesign for Large-Scale Wireless Networked Control Systems [80.30532872347668]
  Wireless Networked Control Systems (WNCSs) are essential to Industry 4.0, enabling flexible control in applications, such as drone swarms and autonomous robots. We propose a practical WNCS model that captures correlated dynamics among multiple control loops with spatially distributed sensors and actuators sharing limited wireless resources over multi-state Markov block-fading channels. We develop a Deep Reinforcement Learning (DRL) algorithm that efficiently handles the hybrid action space, captures communication-control correlations, and ensures robust training despite sparse cross-domain variables and floating control inputs.
  arXiv  Detail & Related papers  (2024-10-15T06:28:21Z)
• Integrating Reinforcement Learning and Model Predictive Control with Applications to Microgrids [14.389086937116582]
  This work proposes an approach that integrates reinforcement learning and model predictive control (MPC) to solve finite-horizon optimal control problems efficiently.<n>Our approach aims to mitigate this issue by decoupling the decision on the discrete variables from the decision on the continuous variables.<n>In the proposed approach, reinforcement learning determines the discrete decision variables and simplifies the online problem of the MPC controller from a mixed-integer linear program to a linear program.
  arXiv  Detail & Related papers  (2024-09-17T15:17:16Z)
• A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading [62.34538208323411]
  We propose a multi-head ensemble multi-task learning (MEMTL) approach with a shared backbone and multiple prediction heads (PHs) MEMTL outperforms benchmark methods in both the inference accuracy and mean square error without requiring additional training data.
  arXiv  Detail & Related papers  (2023-09-02T11:01:16Z)
• Pontryagin Optimal Control via Neural Networks [19.546571122359534]
  We integrate Neural Networks with the Pontryagin's Maximum Principle (PMP), and propose a sample efficient framework NN-PMP-Gradient. The resulting controller can be implemented for systems with unknown and complex dynamics. Compared with the widely applied model-free and model-based reinforcement learning (RL) algorithms, our NN-PMP-Gradient achieves higher sample-efficiency and performance in terms of control objectives.
  arXiv  Detail & Related papers  (2022-12-30T06:47:03Z)
• Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning [89.31889875864599]
  We propose an efficient model-based reinforcement learning algorithm for learning in multi-agent systems. Our main theoretical contributions are the first general regret bounds for model-based reinforcement learning for MFC. We provide a practical parametrization of the core optimization problem.
  arXiv  Detail & Related papers  (2021-07-08T18:01:02Z)
• Consensus Multi-Agent Reinforcement Learning for Volt-VAR Control in Power Distribution Networks [8.472603460083375]
  We propose consensus multi-agent deep reinforcement learning algorithm to solve the VVC problem. The proposed algorithm allows individual agents to learn a group control policy using local rewards. Numerical studies on IEEE distribution test feeders show that our proposed algorithm matches the performance of single-agent reinforcement learning benchmark.
  arXiv  Detail & Related papers  (2020-07-06T18:21:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.