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Unsupervised Federated Quantum GAN for Optimizing Wireless Communications Systems

Triwidyastuti Jamaluddin, Bhaskara Narottama, Soo Young Shin

Published Jun 19, 2023• 0 citations
Publisher

Abstract

In this paper, an unsupervised federated quantum generative adversarial network (UF-QGAN) is proposed to handle optimization issues in wireless communications. The proposed UF-QGAN considers unsupervised learning so that it could work without labeling for the training dataset. By adopting the federated framework, the computational load could be distributed between the cloud and edge. As a representative example, the proposed UFQGAN is applied for non-orthogonal multiple access (NOMA). Achievable sum rate and complexity are investigated for the performance measure. Simulation results show that the UF-QGAN, which not requires data labeling, achieved a similar achievable sum rate compared to the supervised learning scheme

DOI
10.36227/techrxiv.22585060.v2

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In this paper, an unsupervised federated quantum generative adversarial network (UF-QGAN) is proposed to handle optimization issues in wireless communications. The proposed UF-QGAN considers unsupervised learning so that it could work without labeling for the training dataset. By adopting the federated framework, the computational load could be distributed between the cloud and edge. As a representative example, the proposed UFQGAN is applied for non-orthogonal multiple access (NOMA). Achievable sum rate and complexity are investigated for the performance measure. Simulation results show that the UF-QGAN, which not requires data labeling, achieved a similar achievable sum rate compared to the supervised learning scheme
In this paper, an unsupervised federated quantum generative adversarial network (UF-QGAN) is proposed to handle optimization issues in wireless communications. The proposed UF-QGAN considers unsupervised learning so that it could work without labeling for the training dataset. By adopting the federated framework, the computational load could be distributed between the cloud and edge. As a representative example, the proposed UFQGAN is applied for non-orthogonal multiple access (NOMA). Achievable sum rate and complexity are investigated for the performance measure. Simulation results show that the UF-QGAN, which not requires data labeling, achieved a similar achievable sum rate compared to the supervised learning scheme
In this paper, an unsupervised federated quantum generative adversarial network (UF-QGAN) is proposed to handle optimization issues in wireless communications. The proposed UF-QGAN considers unsupervised learning so that it could work without labelling for the training dataset. By adopting the federated framework, the computational load could be distributed between the cloud and edge. As a representative example, the proposed UF-QGAN is applied for non-orthogonal multiple access (NOMA). Achievable sum rate and complexity are investigated for the performance measure. Simulation results show that the UF-QGAN, which not requires data labeling, achieved a similar achievable sum rate compared to the supervised learning scheme.
The following study introduces FT-QNN, a federated and quantum teleportation –based quantum neural network, utilized to optimize resource allocation for future wireless communications. The proposed FT-QNN consists of edge quantum neural networks (QNNs) and a cloud QNN, while quantum teleportation allows the cloud QNN to obtain the outputs of edge QNNs without requiring prior measurements on the output states, allowing the cloud to process the outputs directly as quantum states. As a particular case to demonstrate its applicability for wireless resource allocation, FT-QNN is then employed to optimize transmit power allocation coefficients in a power domain non-orthogonal multiple access (NOMA)-based system, aiming to maximize the achievable sum-rate. FT-QNN yields lower complexity compared to a distributed QNN scheme without quantum teleportation, while the numerical results also demonstrated that the FT-QNN is capable to achieve a similar sum-rate compared to the scheme without quantum teleportation.
In increasingly complex digital environments, Intrusion Detection Systems (IDS) are essential for maintaining network security. Traditional machine learning techniques face challenges, including difficulties with high-dimensional data and performance issues when dealing with unbalanced datasets. Although Generative Adversarial Networks (GANs) provide an acceptable alternative by improving data creation, their traditional implementations are computationally demanding and have limitations when it comes to examining intricate data distributions. Quantum GANs (QGANs) use the benefits of quantum computing to solve these problems. Therefore, a distributed approach improves load balancing and mitigate the impact of quantum noise for NISQ devices. The proposed model efficiently learns the distribution of benign data by employing a federated hybrid QGAN architecture that combines a quantum generators with a classical discriminators. This allows the model to produce samples that closely mimic patterns found in real data. The generator in the proposed QGAN is a variational quantum circuit (VQC), and the discriminator is a neural network. Tested on the NSL-KDD dataset, the federated QGAN offers a promising option for next-generation IDS.
Quantum federated learning (QFL) combines the robust data processing of quantum computing with the privacy-preserving features of federated learning (FL). However, in large-scale wireless networks, optimizing sum-rate is crucial for unlocking the true potential of QFL, facilitating effective model sharing and aggregation as devices compete for limited bandwidth amid dynamic channel conditions and fluctuating power resources. This paper studies a novel sum-rate maximization problem within a muti-channel QFL framework, specifically designed for non-orthogonal multiple access (NOMA)-based large-scale wireless networks. We develop a sum-rate maximization problem by jointly considering quantum device’s channel selection and transmit power. Our formulated problem is a non-convex, mixed-integer non-linear programming (MINLP) challenge that remains non-deterministic polynomial time (NP)-hard even with specified channel selection parameters. The complexity of the problem motivates us to create an effective iterative optimization approach that utilizes the sophisticated quantum approximate optimization algorithm (QAOA) to derive high-quality approximate solutions. Additionally, our study presents the first theoretical exploration of QFL convergence properties under full device participation, rigorously analyzing real-world scenarios with nonconvex loss functions, diverse data distributions, and the effects of quantum shot noise. Extensive simulation results indicate that our multi-channel NOMA-based QFL framework enhances model training and convergence behavior, surpassing conventional algorithms in terms of accuracy and loss. Moreover, our quantum-centric joint optimization approach achieves more than a 100% increase in sum-rate while ensuring rapid convergence, significantly outperforming the state-of-the-arts.
Quantum federated learning (QFL) combines the robust data processing of quantum computing with the privacy-preserving features of federated learning (FL). However, in large-scale wireless networks, optimizing sum-rate is crucial for unlocking the true potential of QFL, facilitating effective model sharing and aggregation as devices compete for limited bandwidth amid dynamic channel conditions and fluctuating power resources. This paper studies a novel sum-rate maximization problem within a muti-channel QFL framework, specifically designed for non-orthogonal multiple access (NOMA)-based large-scale wireless networks. We develop a sum-rate maximization problem by jointly considering quantum device's channel selection and transmit power. Our formulated problem is a non-convex, mixed-integer nonlinear programming (MINLP) challenge that remains non-deterministic polynomial time (NP)-hard even with specified channel selection parameters. The complexity of the problem motivates us to create an effective iterative optimization approach that utilizes the sophisticated quantum approximate optimization algorithm (QAOA) to derive high-quality approximate solutions. Additionally, our study presents the first theoretical exploration of QFL convergence properties under full device participation, rigorously analyzing real-world scenarios with nonconvex loss functions, diverse data distributions, and the effects of quantum shot noise. Extensive simulation results indicate that our multi-channel NOMA-based QFL framework enhances model training and convergence behavior, surpassing conventional algorithms in terms of accuracy and loss. Moreover, our quantum-centric joint optimization approach achieves more than a 100% increase in sum-rate while ensuring rapid convergence, significantly outperforming the state-of-the-arts.