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Quantum Neural Network With Parallel Training for Wireless Resource Optimization
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DOI：10.1109/TMC.2023.3321467.png)
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摘要
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A quantum neural network with parallel training (called PS-QNN) is presented in this study to optimize wireless resource allocation. Instead of sending the whole dataset, each edge only requires to send the statistical parameters of the dataset; hence reducing the dimension of the training data. As a particular case, the proposed PS-QNN is utilized to optimize transmit precoding and power allocation in non-orthogonal multiple access with multiple-input and multiple-output antennas (MIMO-NOMA). Compared to the conventional training method, analysis shows that the proposed parallel training yields a lower complexity, while achieving a comparable sum rate compared to conventional method.
Keyword:
Training
Wireless communication
Optimization
NOMA
Precoding
Transmitting antennas
Qubit
Quantum neural networks
unsupervised learning
non-orthogonal multiple access
期刊
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9.2
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5.6K
被引数:
1.8W
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