Quantum Neural Networks for Resource Allocation in Wireless Communications

被引：16

作者：

Narottama, Bhaskara ^{[1
]}

Shin, Soo Young ^{[1
]}

机构：

[1] Kumoh Natl Inst Technol, Dept It Convergence Engn, WENS Lab, Gumi 39177, South Korea

来源：

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS | 2022年 / 21卷 / 02期

基金：

新加坡国家研究基金会;

关键词：

NOMA; Wireless communication; Resource management; Neurons; Encoding; Artificial neural networks; Biological neural networks; 6G; B5G; non-orthogonal multiple access; quantum neural networks; wireless communications; NONORTHOGONAL MULTIPLE-ACCESS; NOMA; SPECTRUM;

D O I：

10.1109/TWC.2021.3102139

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This study exploits a quantum neural network (QNN) for resource allocation in wireless communications. A QNN is presented to reduce time complexity while still maintaining performance. Moreover, a reinforcement-learning- inspired QNN (RL-QNN) is presented to improve the perfor- mance. Quantum circuit design of the QNN is presented to ensure the practical implementation in noisy intermediate-scale quantum (NISQ) computers. For the QNN, the complexity and the number of required qubits are analyzed as well. As a particular use case, the QNN is utilized for user grouping in non-orthogonal multiple access. The results reveal that the QNN schemes have lower complexities and similar performance in terms of the achievable sum rate when compared with that of the classical neural network.

引用

页码：1103 / 1116

页数：14

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