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

Research Article

SNR Analysis of the Millimeter Wave MIMO with Lens Antenna Array

Download
237 downloads
  • @INPROCEEDINGS{10.1007/978-3-319-73564-1_50,
        author={Min Zhang and Jianxin Dai and Chonghu Cheng and Zhiliang Huang},
        title={SNR Analysis of the Millimeter Wave MIMO with Lens Antenna Array},
        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={Millimeter wave Lens antenna array Signal-to-noise ratio Path-division multiplexing AoA/AoD},
        doi={10.1007/978-3-319-73564-1_50}
    }
    
  • Min Zhang
    Jianxin Dai
    Chonghu Cheng
    Zhiliang Huang
    Year: 2018
    SNR Analysis of the Millimeter Wave MIMO with Lens Antenna Array
    MLICOM
    Springer
    DOI: 10.1007/978-3-319-73564-1_50
Min Zhang1,*, Jianxin Dai1,*, Chonghu Cheng1,*, Zhiliang Huang2,*
  • 1: Nanjing University of Posts and Telecommunications
  • 2: Zhejiang Normal University
*Contact email: 2810729610@qq.com, daijx@njupt.edu.cn, chengch@njupt.edu.cn, zlhuang@zjnu.cn

Abstract

The lens antenna array is typically composed of an electromagnetic (EM) lens and has elements in the focal area of the lens in order to achieve its large antenna gain. In this paper, we first analyze the response model of the lens antenna array, and conclude that the model follows the “sinc” function. The lens array is then applied to a MIMO system that allows millimeter-wave input and the use of new path-division multiplexing. On this basis, we model the channel of the system to derive the channel impulse response, which follows the “sinc sinc” function. Finally, the beamforming process is performed at the receiving end to obtain the received signal, and the signal-to-noise ratio expression is analyzed and optimized to obtain the maximum signal-to-noise ratio (SNR) of the system and the system performance is simulated.