Hybrid Quantum Convolutional Neural Network-Aided Pilot Assignment in Cell-Free Massive MIMO Systems Article Swipe

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Dat Nguyen , Nguyen Xuan Tung , Seon-Geun Jeong , Trinh Van Chien , Lajos Hanzo , Won–Joo Hwang ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2507.06585

A sophisticated hybrid quantum convolutional neural network (HQCNN) is conceived for handling the pilot assignment task in cell-free massive MIMO systems, while maximizing the total ergodic sum throughput. The existing model-based solutions found in the literature are inefficient and/or computationally demanding. Similarly, conventional deep neural networks may struggle in the face of high-dimensional inputs, require complex architectures, and their convergence is slow due to training numerous hyperparameters. The proposed HQCNN leverages parameterized quantum circuits (PQCs) relying on superposition for enhanced feature extraction. Specifically, we exploit the same PQC across all the convolutional layers for customizing the neural network and for accelerating the convergence. Our numerical results demonstrate that the proposed HQCNN offers a total network throughput close to that of the excessive-complexity exhaustive search and outperforms the state-of-the-art benchmarks.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2507.06585
PDF: https://arxiv.org/pdf/2507.06585
OA Status: green
OpenAlex ID: https://openalex.org/W4416063007

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OpenAlex ID: https://openalex.org/W4416063007

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DOI: https://doi.org/10.48550/arxiv.2507.06585

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Title: Hybrid Quantum Convolutional Neural Network-Aided Pilot Assignment in Cell-Free Massive MIMO Systems

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-07-09

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Authors: Dat Nguyen, Nguyen Xuan Tung, Seon-Geun Jeong, Trinh Van Chien, Lajos Hanzo, Won–Joo Hwang

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Landing page: https://arxiv.org/abs/2507.06585

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PDF URL: https://arxiv.org/pdf/2507.06585

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Cited by: 0

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