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Channel Estimation and Interference Management in Multi-User MIMO Systems Using Integrated Deep Learning and Quantum Computing

Shutong Yao

Published Nov 15, 2024• 0 citations
Publisher

Abstract

Abstract This study presents a novel approach combining deep learning with quantum computing for channel estimation and interference management in multi-user MIMO systems. Addressing the limitations of traditional channel estimation methods in complex wireless communication environments, we introduce quantum computing techniques, including quantum Fourier transforms and quantum state reconstruction algorithms, to enhance the accuracy and efficiency of channel estimation. Building on the results from quantum computing, we further optimize the outcomes using deep learning networks, focusing specifically on interference cancellation and data compression. In the experimental setup, we simulated the channel matrix of a multi-user MIMO system on a quantum computing platform and trained a convolutional neural network (CNN) for deep learning optimization. The findings demonstrate that the integration of quantum computing and deep learning substantially enhances system performance, particularly in managing interference and processing data in complex channel environments. This study not only provides new technical methods for channel estimation and interference management in multi-user communication systems but also paves the way for further research into the combined application of quantum computing and deep learning.

DOI
10.21203/rs.3.rs-5051284/v1

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Abstract This study presents a novel approach combining deep learning with quantum computing for channel estimation and interference management in multi-user MIMO systems. Addressing the limitations of traditional channel estimation methods in complex wireless communication environments, we introduce quantum computing techniques, including quantum Fourier transforms and quantum state reconstruction algorithms, to enhance the accuracy and efficiency of channel estimation. Building on the results from quantum computing, we further optimize the outcomes using deep learning networks, focusing specifically on interference cancellation and data compression. In the experimental setup, we simulated the channel matrix of a multi-user MIMO system on a quantum computing platform and trained a convolutional neural network (CNN) for deep learning optimization. The findings demonstrate that the integration of quantum computing and deep learning substantially enhances system performance, particularly in managing interference and processing data in complex channel environments. This study not only provides new technical methods for channel estimation and interference management in multi-user communication systems but also paves the way for further research into the combined application of quantum computing and deep learning.
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We introduce a hybrid Quantum Neural Networks (QNN) architecture for the efficient user scheduling in 5G/Beyond 5G (B5G) massive Multiple Input Multiple Output (MIMO) systems, addressing the scalability issues of traditional methods. By leveraging statistical Channel State Information (CSI), our model reduces computational overhead and enhances spectral efficiency. It integrates classical neural networks with a variational quantum circuit kernel, outperforming classical Convolutional Neural Networks (CNN) and maintaining robust performance in noisy channels. This demonstrates the potential of quantum-enhanced Machine Learning (ML) for wireless scheduling.
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