Research on transmission line communication relaying based on deep 
learning route optimization algorithm

Zhanjun Ma; Rui Fu; Lei Feng; Tao Wang; Xuelati Simayi

doi:10.1117/12.3011216

20 October 2023 Research on transmission line communication relaying based on deep learning route optimization algorithm

Zhanjun Ma, Rui Fu, Lei Feng, Tao Wang, Xuelati Simayi

Author Affiliations +

Proceedings Volume 12814, Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023); 128140R (2023) https://doi.org/10.1117/12.3011216
Event: Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023), 2023, Chongqing, China

Abstract

The current conventional transmission line communication relay selection strategy is mainly based on one-way relay system, which leads to large system capacity occupation due to the lack of calculation of relay transmission rate. In this regard, the study of transmission line communication relaying based on deep learning routing optimization algorithm is proposed. A two-way model of transmission line communication network is constructed by using double-half duplex mode to build orthogonal channels. The relay transmission rate is calculated, and the selection of relay nodes is completed by constructing a loss function and mapping the system operation. In the experiments, the performance of the proposed method is verified for relay selection. The experimental results show that the proposed method has a larger system capacity and a smaller space occupation when relay node selection is performed, and has a more desirable relay selection performance.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhanjun Ma, Rui Fu, Lei Feng, Tao Wang, and Xuelati Simayi "Research on transmission line communication relaying based on deep learning route optimization algorithm", Proc. SPIE 12814, Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023), 128140R (20 October 2023); https://doi.org/10.1117/12.3011216

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