This paper investigates advanced signal processing techniques to address the critical challenge of Multi-User Interference (MUI) within the demanding framework of 6th Generation (6G) wireless systems. The study presents a direct comparative analysis between classical deep learning and emerging quantum machine learning paradigms. We first establish the capabilities of Convolutional Neural Networks (CNNs), a state-of-the-art deep learning model, in handling the complex, non-ideal channel environments of modern wireless communications. Subsequently, we explore the theoretical advantages and practical performance of Quantum Convolutional Neural Networks (QCNNs), positing them as a next-generation solution for processing unprecedented data complexity. Through rigorous simulation in a multi-user SIMO uplink scenario, this paper demonstrates that QCNN consistently outperforms its classical CNN counterpart in both signal detection accuracy and overall system sum rate, particularly in low signal-to-noise ratio (SNR) regimes, all while utilizing significantly fewer model parameters. The core contribution of this work is to provide direct empirical evidence of the potential quantum advantage offered by QCNNs for the complex signal processing tasks inherent to future 6G systems.
This paper investigates advanced signal processing techniques to address the critical challenge of Multi-User Interference (MUI) within the demanding framework of 6th Generation (6G) wireless systems. The study presents a direct comparative analysis between classical deep learning and emerging quantum machine learning paradigms. We first establish the capabilities of Convolutional Neural Networks (CNNs), a state-of-the-art deep learning model, in handling the complex, non-ideal channel environments of modern wireless communications. Subsequently, we explore the theoretical advantages and practical performance of Quantum Convolutional Neural Networks (QCNNs), positing them as a next-generation solution for processing unprecedented data complexity. Through rigorous simulation in a multi-user SIMO uplink scenario, this paper demonstrates that QCNN consistently outperforms its classical CNN counterpart in both signal detection accuracy and overall system sum rate, particularly in low signal-to-noise ratio (SNR) regimes, all while utilizing significantly fewer model parameters. The core contribution of this work is to provide direct empirical evidence of the potential quantum advantage offered by QCNNs for the complex signal processing tasks inherent to future 6G systems.
The emergence of sixth-generation (6G) communication networks is expected to revolutionize wireless systems by enabling ultra-high data rates, ultra-low latency, and massive connectivity. Efficient resource management is a critical challenge due to the dynamic and heterogeneous nature of 6G environments. Traditional optimization techniques are insufficient to handle the complexity of such networks, leading to the adoption of artificial intelligence (AI)-driven approaches. This paper presents a comprehensive review of AI techniques for efficient resource management in 6G networks, focusing on hybrid quantum duplet-convolutional neural network (CNN) models. These models integrate quantum computing with classical deep learning to enhance computational efficiency and optimization capability. The study analyzes recent literature, covering deep learning, reinforcement learning, federated learning, and quantum machine learning approaches. A comparative analysis highlights the advantages of hybrid quantum-CNN models in terms of scalability, adaptability, and performance. The paper also discusses emerging trends such as AI-native network design, edge intelligence, and quantum-enhanced optimization. Furthermore, key challenges including computational complexity, energy consumption, and quantum hardware limitations are examined. The findings suggest that hybrid quantum deep learning models are promising for addressing resource management challenges in future 6G networks.
This paper presents a comprehensive evaluation of the potential of Quantum Convolutional Neural Networks (QCNNs) in comparison to classical Convolutional Neural Networks (CNNs) and Artificial / Classical Neural Network (ANN) models. With the increasing amount of data, utilizing computing methods like CNN in real-time has become challenging. QCNNs overcome this challenge by utilizing qubits to represent data in a quantum environment and applying CNN structures to quantum computers. The time and accuracy of QCNNs are compared with classical CNNs and ANN models under different conditions such as batch size and input size. The maximum complexity level that QCNNs can handle in terms of these parameters is also investigated. The analysis shows that QCNNs have the potential to outperform both classical CNNs and ANN models in terms of accuracy and efficiency for certain applications, demonstrating their promise as a powerful tool in the field of machine learning.
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.
The emergence of Beyond 5G (B5G) and 6G networks introduces advanced capabilities in speed, latency, and connectivity, while also amplifying the risk of sophisticated security threats such as False Base Stations (FBS). These rogue entities imitate legitimate base stations to intercept data, perform man-in-the-middle attacks, or disrupt services. Researchers have explored conventional machine learning methods for FBS detection, but these methods often struggle to address the high-dimensional and dynamic nature of next-generation networks. This paper proposes a Quanvolutional Neural Network (QNN) framework as a quantum-enhanced solution for FBS detection. QNN utilizes quantum phenomena such as superposition and entanglement to perform complex feature transformations, enabling more expressive data representations than classical models. We evaluate the proposed QNN against a traditional Convolutional Neural Network (CNN) using a classification task designed to simulate the detection of FBS. Results demonstrate that although the QNN exhibits slower convergence during early training, it ultimately outperforms the CNN across all key evaluation metrics. The QNN achieves higher accuracy (90.65%), improved recall, lower false positive rate, and a superior F1 score, while also attaining a lower final training loss. These findings suggest that QNNs have strong potential for real-time, localized threat detection in AInative 6G architectures. Future work will explore deployment on Noisy Intermediate-Scale Quantum (NISQ) hardware and evaluate model performance under realistic noise conditions. This study contributes to ongoing research on integrating quantum machine learning into secure and intelligent wireless communication systems
This study investigates the integration of quantum computing, classical methods, and deep learning techniques for enhanced image processing in dynamic 6G networks, while also addressing essential aspects of copyright technology and detection. Our findings indicate that quantum methods excel in rapid edge detection and feature extraction but encounter difficulties in maintaining image quality compared to classical approaches. In contrast, classical methods preserve higher image fidelity but struggle to satisfy the real-time processing requirements of 6G applications. Deep learning techniques, particularly CNNs, demonstrate potential in complex image analysis tasks but demand substantial computational resources. To promote the ethical use of AI-generated images, we introduce copyright detection mechanisms that employ advanced algorithms to identify potential infringements in generated content. This integration improves adherence to intellectual property rights and legal standards, supporting the responsible implementation of image processing technologies. We suggest that the future of image processing in 6G networks resides in hybrid systems that effectively utilize the strengths of each approach while incorporating robust copyright detection capabilities. These insights contribute to the development of efficient, high-performance image processing systems in next-generation networks, highlighting the promise of integrated quantum-classical–classical deep learning architectures within 6G environments.
While machine learning technology evolves fast with the development of computing power and new algorithms, integrating theories of quantum mechanics gives novel opportunities to it. This paper focuses on the comparative analysis of Quantum Convolutional Neural Networks (QCNN) and traditional Convolutional Neural Networks (CNN), which are important techniques in the domain of machine learning, especially image processing. By doing a literature review and analysis, the comparison includes performance differences, application potential, and inherent challenges of QCNN. Although QCNN is comparably novel, it does exhibit superiority over CNN in specific cases theoretically like high dimensional and complex datasets. QCNN currently is facing problems due to hardware constraints and immaturity in quantum computing and doesn't show superior performance when running on actual quantum machines. With the development of quantum computing and hardware, QCNN has a strong potential for use in machine learning. The research compares the current performance of QCNN and CNN to make suggestions about applying them in real cases and guide the path for future research.