Quantum Neural Network With Parallel Training for Wireless Resource Optimization
- Authors
- Narottama, Bhaskara; Jamaluddin, Triwidyastuti; Shin, Soo Young
- Issue Date
- May-2024
- Publisher
- IEEE COMPUTER SOC
- Keywords
- Training; Wireless communication; Optimization; NOMA; Precoding; Transmitting antennas; Qubit; Quantum neural networks; unsupervised learning; non-orthogonal multiple access
- Citation
- IEEE TRANSACTIONS ON MOBILE COMPUTING, v.23, no.5, pp 5835 - 5847
- Pages
- 13
- Journal Title
- IEEE TRANSACTIONS ON MOBILE COMPUTING
- Volume
- 23
- Number
- 5
- Start Page
- 5835
- End Page
- 5847
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/28854
- DOI
- 10.1109/TMC.2023.3321467
- ISSN
- 1536-1233
1558-0660
- Abstract
- 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.
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