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| Antenna Selection Algorithm Using Capacity Correlation Dictionary for 5G Millimeter Wave Massive Antenna Systems |
| WANG Qi① ZHAO Xiongwen①②③ ZHANG Rui② LI Shu① WANG Mengjun③ SUN Shaohui③ |
①(School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)
②(National Key Laboratory of Electromagnetic Environment, China Research Institute of Radio Wave Propagation, Qingdao 266107, China)
③(State Key Laboratory of Wireless Mobile Communictions, China Academy of Telecommunication Technology Beijing,100191, China) |
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Abstract In order to solve the limitation to use the traditional antenna selection algorithm in a massive antenna for fifth Generation (5G) system, a random antenna selection algorithm based on the dictionary of capacity correlation in multi-users scenario is proposed. Before the dictionary is built, the correlation characteristics among antenna units are abstracted by the interactions between the base station and users at different locations. It needs to search the antenna unit with optimal capacity to connect one Radio Frequency (RF) link, and the reminder RF links can select the antennas randomly which have the large correlation with the optimal one in the dictionary. The method is applicable to massive antennas system because of its low complexity and stability. Moreover, there is no need to renew the dictionary for coming new users. In addition, the massive antenna channel measurements in an open office including line-of-sight and none-line-of-sight at 26 GHz are carried out to analyze the capacity correlations among the antenna units located in different rows as well as to verify the system capacity performance with the random antenna selection algorithm proposed in this work.
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Received: 20 March 2017
Published: 28 August 2017
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| Fund:The National Key Laboratory of Electromagnetic Environment(201600012), China Research Institute of Radiowave Propagation, The State Key Laboratory of Wireless Mobile Communications, China Academy of Telecommunications Technology (DTimo.15.094), The Fundamental Research Funds for the Central Universities (2015XS19) |
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Corresponding Authors:
ZHAO Xiongwen
E-mail: zhaoxw@ncepu.edu.cn
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