aiqbench analysis
This research applies the Quantum Approximate Optimization Algorithm (QAOA) to solve the combinatorial challenge of pilot assignment in cell-free massive MIMO systems, addressing a bottleneck that is computationally intractable for classical computers. By reformulating the problem to minimize pilot contamination directly within a quantum framework, the study demonstrates that quantum-assisted optimization can achieve performance comparable to exhaustive search methods. The work highlights a specific pathway where quantum computing offers a practical advantage over classical approaches for large-scale wireless network optimization problems.
QAOA-Based Pilot Assignment for Cell-Free Massive MIMO Systems
arXiv:2609.23657v1 Announce Type: new 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.
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.
arXiv:2609.23657 [quant-ph]
(or arXiv:2609.23657v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2609.23657 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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