Efficient resource management in 6G communication networks using hybrid quantum deep learning model. (March 2023)

• Record Type: Journal Article
• Authors: Ashwin, M; Alqahtani, Abdulrahman Saad; Mubarakali, Azath; Sivakumar, B
• Is Part Of: Computers & electrical engineering. Volume 106(2023)
• Publication Date: 2023-03
• Subjects: 5G network -- 6G network -- Convolutional neural network -- Recurrent neural network -- Hybrid quantum deep learning model; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108565
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: The sixth generation (6G) wireless communication network is expected to provide wide-ranging coverage, short latency, cost effectiveness, lower power consumption, and a high level of security. The Quality of Service (QoS) can be improved with the help of proper resource management by utilizing Artificial Intelligence and Machine Learning procedures. The proposed Hybrid Quantum Deep Learning (HQDL) model which comprises of Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). The CNN achieves network reconfiguration, resource distribution and slice collection while RNN is utilized for error proportion, load balancing etc. The future model QoS performance is evaluated by utilizing numerous indefinite devices, slice features and congestion environments. The proposed model achieved an overall accuracy of 97.16% that replicates its pertinence.