Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part I

Research Article

Cross-Entropy Optimization Oriented Antenna Selection for Clustering Management in Multiuser MIMO Networks

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  • @INPROCEEDINGS{10.1007/978-3-319-73564-1_51,
        author={Xinyu Zhang and Jing Guo and Qiuyi Cao and Nan Zhao},
        title={Cross-Entropy Optimization Oriented Antenna Selection for Clustering Management in Multiuser MIMO Networks},
        proceedings={Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part I},
        proceedings_a={MLICOM},
        year={2018},
        month={2},
        keywords={Antenna selection Cross-entropy optimization Clustering management Interference alignment},
        doi={10.1007/978-3-319-73564-1_51}
    }
    
  • Xinyu Zhang
    Jing Guo
    Qiuyi Cao
    Nan Zhao
    Year: 2018
    Cross-Entropy Optimization Oriented Antenna Selection for Clustering Management in Multiuser MIMO Networks
    MLICOM
    Springer
    DOI: 10.1007/978-3-319-73564-1_51
Xinyu Zhang1,*, Jing Guo1,*, Qiuyi Cao1,*, Nan Zhao1,*
  • 1: Dalian University of Technology
*Contact email: xinyuzhang@mail.dlut.edu.cn, guojing94@mail.dlut.edu.cn, cccqiu_yi@163.com, zhaonan@dlut.edu.cn

Abstract

In this paper, antenna selection (AS) is considered for clustering management (CM) to improve the spectrum efficiency of asymmetric interference networks. Through the proposed CM scheme, the whole network can be divided into several clusters, which will lead to a relative redundance of antenna resource for each interference alignment (IA) pair in the IA cluster. Therefore, the AS technique is adopted to improve the performance through selecting the optimal antenna combination for IA pairs. Considering the high computational complexity of the exhaustive search (ES) AS method, the cross-entropy optimization (CEO) algorithm is used to perform the IA technique, which can achieve relatively high performance with low computational complexity. From the simulation results, we can find that the proposed AS method in clustering management can further enhance the performance of the IA-based network.