Detailed Information
Cited 0 time in 
Cited 0 time in 
Cited 0 time in
Metadata Downloads
Quantum Neural Network With Parallel Training for Wireless Resource Optimization
- 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
- 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.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - School of Electronic Engineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
Related Researcher
- SHIN, SOO YOUNG
- College of Engineering (School of Electronic Engineering)