典型文献
Cloud-Assisted Distributed Edge Brains for Multi-Cell Joint Beamforming Optimization for 6G
文献摘要：
In 5G networks,optimization of antenna beam weights of base stations has become the key application of AI for network optimization.For 6G,higher frequency bands and much denser cells are ex-pected,and the importance of automatic and accurate beamforming assisted by AI will become more promi-nent.In existing network,servers are"patched"to network equipment to act as a centralized brain for model training and inference leading to high trans-mission overhead,large inference latency and poten-tial risks of data security.Decentralized architectures have been proposed to achieve flexible parameter con-figuration and fast local response,but it is inefficient in collecting and sharing global information among base stations.In this paper,we propose a novel solution based on a collaborative cloud edge architecture for multi-cell joint beamforming optimization.We ana-lyze the performance and costs of the proposed solu-tion with two other architectural solutions by simula-tion.Compared with the centralized solution,our so-lution improves prediction accuracy by 24.66％,and reduces storage cost by 83.82％.Compared with the decentralized solution,our solution improves predic-tion accuracy by 68.26％,and improves coverage per-formance by 0.4dB.At last,the future research work is prospected.
文献关键词：
中图分类号：
作者姓名：
Juan Deng;Kaicong Tian;Qingbi Zheng;Jielin Bai;Kuo Cui;Yitong Liu;Guangyi Liu
作者机构：
Future Research Lab,China Mobile Research Institute,Beijing 100032,China;Beijing University of Posts and Telecommunications,Beijing 100876,China
文献出处：
引用格式：
[1]Juan Deng;Kaicong Tian;Qingbi Zheng;Jielin Bai;Kuo Cui;Yitong Liu;Guangyi Liu-.Cloud-Assisted Distributed Edge Brains for Multi-Cell Joint Beamforming Optimization for 6G)[J].中国通信（英文版）,2022(03):36-49
A类：
Brains
B类：
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AB值：
0.607253
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