QAOA-Based Pilot Assignment for Cell-Free Massive MIMO Systems
Abstract
Pilot contamination is a major challenge in cell-free massive multiple-input multiple-output (MIMO) systems, where the limited number of orthogonal pilots makes pilot reuse unavoidable. Since the pilot assignment solution space grows exponentially with the number of users, finding the global optimum becomes computationally challenging on classical computers. Recent advances in quantum computing provide a promising approach for solving such large-scale combinatorial optimization problems. In this paper, we reformulate the assignment problem as a quantum-compatible optimization problem, enabling it to be directly solved by the Quantum Approximate Optimization Algorithm (QAOA). Specifically, the pilot contamination objective and assignment constraints are incorporated into the QAOA formulation. This allows the quantum search to focus on valid pilot assignments with low contamination cost. Simulation results show that the proposed method achieves performance close to exhaustive search, demonstrating the potential of quantum-assisted optimization for pilot assignment in future wireless systems.
Related
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- Toward Fault-Tolerant Variational Optimization: QAOA under [[4,2,2]] Error Detection
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BibTeX
@misc{ma2026qaoabased,
title = {{QAOA-Based Pilot Assignment for Cell-Free Massive MIMO Systems}},
author = {Xiaoyu Ma and Fang Fang and Xianbin Wang},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.23657},
eprint = {2609.23657},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.23657},
}
RIS
TY - UNPB
TI - QAOA-Based Pilot Assignment for Cell-Free Massive MIMO Systems
AU - Xiaoyu Ma
AU - Fang Fang
AU - Xianbin Wang
PY - 2026
DA - 2026/09/20
PB - arXiv
DO - 10.48550/arxiv.2609.23657
UR - https://arxiv.org/abs/2609.23657
AB - Pilot contamination is a major challenge in cell-free massive multiple-input multiple-output (MIMO) systems, where the limited number of orthogonal pilots makes pilot reuse unavoidable. Since the pilot assignment solution space grows exponentially with the number of users, finding the global optimum becomes computationally challenging on classical computers. Recent advances in quantum computing provide a promising approach for solving such large-scale combinatorial optimization problems. In this paper, we reformulate the assignment problem as a quantum-compatible optimization problem, enabling it to be directly solved by the Quantum Approximate Optimization Algorithm (QAOA). Specifically, the pilot contamination objective and assignment constraints are incorporated into the QAOA formulation. This allows the quantum search to focus on valid pilot assignments with low contamination cost. Simulation results show that the proposed method achieves performance close to exhaustive search, demonstrating the potential of quantum-assisted optimization for pilot assignment in future wireless systems.
ER -