Dr. Jian Xu Profile

Dr. Jian Xu

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Proceedings Article | 18 November 2024 Paper

Design and implementation of public-private integrated communication terminals for new power system

Shengbiao Cao, Jian Xu, Ran Liu, Di Liu, RunAn Yang

Proceedings Volume 13398, 133981H (2024) https://doi.org/10.1117/12.3049725

KEYWORDS: Data communications, Network security, Design, Data transmission, Reliability, Control systems, Power grids

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5G technology has been widely used in power systems due to its high speed, low latency, and wide connectivity. At the same time, the power wireless private network designed specifically for the power system provides solid support for the stable operation of the power grid with its high reliability, safety, and scalability. With the development of new power systems, the demand for power communication is growing rapidly. By combining the advantages of 5G and power grid, a public dedicated integrated communication terminal is designed to improve the efficiency and intelligence level of power grid communication, while ensuring business security and efficient operation. The application testing of the intelligent distribution station building scheme has verified the terminal's ability to handle massive data, high bandwidth communication, and data security. The electrical performance of the terminal fully meets the standards of the power industry and has broad application prospects in the field of power business adaptation.

Proceedings Article | 12 July 2024 Paper

Star-earth cooperative network channel allocation algorithm based on power grid and deep learning

Jigao Song, Jian Xu, Xuanzhong Wang, Haitao Xu

Proceedings Volume 13185, 131850E (2024) https://doi.org/10.1117/12.3032335

KEYWORDS: Satellites, Network architectures, Deep learning, Mathematical optimization, Antennas, Telecommunications, Satellite communications, Power grids, Design, Computer simulations

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The star earth collaboration network private network can make up for the problems of poor signal coverage, limited bandwidth, low security and high cost that exist in the existing wireless public network. It can provide reliable and stable power system communications. However, the star-earth collaborative network suffers from resource constraints due to limited on-planet spectrum resources, power, computation and storage resources. Therefore, how to allocate these limited channel resources reasonably and efficiently, it is especially important to improve the resource utilization and system performance. Based on this, we establish a star-earth collaborative network architecture to meet the application of power scenarios. To improve the resource allocation efficiency of this network architecture, we model the channel allocation process by sensing the channel allocation state and beam user service request state in the environment through the satellite intelligences of the star-earth collaboration network. And then, we propose a dynamic channel allocation algorithm based on deep learning. We design state, action and reward functions for feedback optimization of channel resource allocation strategy based on deep learning, and a deep learning based channel resource allocation method is proposed to realize the optimal allocation of system channel resources. In the proposed method, the corresponding channel resources for users is allocated based on the channel allocation policy, and environment-based reward gain information is given to optimally update the channel allocation policy. The related simulation results show that our proposed channel resource allocation algorithm for star-terrestrial collaborative network has lower service blocking rate and higher channel utilization than other assistant allocation algorithms.

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