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Quantum-Correlated Simulated Annealing based joint beam-position training for movable array mmWave MIMO communications
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DOI：10.1016/j.icte.2026.05.010.png)
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Abstract
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Millimeter-wave movable antenna arrays enhance channel conditions via spatial optimization, yet joint beam-position training poses a high-dimensional nonconvex challenge. Inspired by quantum tunneling (QT), this paper proposes a Quantum-Correlated Simulated Annealing (QCSA) algorithm. It decomposes the parameter space into transceiver subspaces for complexity reduction, employs adaptive Gaussian sampling with dynamic covariance updating to capture search correlations, introduces a QT-enhanced acceptance criterion to escape local optima, and integrates alternating optimization with oscillatory thermal scheduling to avoid premature convergence. Simulations verify that QCSA achieves 93.52% of the Alternating Coordinate Descent Exhaustive Search (ACD-ES) benchmark’s spectral efficiency with only 7.29% of its training overhead, delivering fast convergence and near-optimal performance under diverse multipath conditions.
Keywords:
Movable antenna arrays
Joint beam-position optimization
Quantum-Correlated Simulated Annealing
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