Design and modeling of 5G base station adaptive antenna feed system

doi:10.11835/j.issn.1000-582X.2023.04.009

Home > Archive>Volume 46, Issue 4, 2023 >89-96. DOI:10.11835/j.issn.1000-582X.2023.04.009

Design and modeling of 5G base station adaptive antenna feed system
DOI:
                        10.11835/j.issn.1000-582X.2023.04.009
                    
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                        [cstr]
                    
Author:
                        SHEN YuhangSHEN Yuhang
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
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WANG ShengWANG Sheng
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
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Affiliation:School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
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Fund Project:Supported by National Natural Science Foundation of China(62001087,62072079).

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Abstract:

To provide a fully automatic antenna adaptive adjustment scheme with advantages of better performance, wider coverage and lower maintenance cost, the key design technologies of intelligent adjustment system of adaptive antenna feed system of 5g-based station are studied from the perspective of signal radiation direction adjustment of antenna panel. An adaptive adjustment strategy for base-station antenna based on deep reinforcement learning is proposed. The adaptive antenna feed system designed with the proposed strategy can use telecom RSRP coverage map as a data source, and obtain the current state of the observed values to automatically analyze data and adjust the antenna panels. In a virtual environment, the system based on reinforcement learning is simulated and trained, and the results are in line with expectations.

Key words:5G base-station;reinforcement learning;antenna feed system;adaptive adjustment;systematic design;simulated analysis

Reference

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沈煜航,王晟.5G基站自适应天馈系统设计与建模[J].重庆大学学报,2023,46(4):89~96

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Received:December 25,2021
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Online: May 12,2023
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