中国邮电高校学报(英文) ›› 2007, Vol. 14 ›› Issue (4): 36-40.doi: 1005-8885 (2007) 04-0036-05

• Wireless • 上一篇    下一篇

Semiparametric theory based MIMO model and perfor- mance analysis

许方敏;许晓东; 张平   

  1. Key Laboratory of Universal Wireless Communications (Beijing University of Posts and Telecommunications) Ministry of Education, Wireless Technology Innovation Institute
  • 收稿日期:2007-01-11 修回日期:1900-01-01 出版日期:2007-12-24
  • 通讯作者: 许方敏

Semiparametric theory based MIMO model and perfor- mance analysis

XU Fang-min; XU Xiao-dong; ZHANG Ping   

  1. Key Laboratory of Universal Wireless Communications (Beijing University of Posts and Telecommunications) Ministry of Education, Wireless Technology Innovation Institute
  • Received:2007-01-11 Revised:1900-01-01 Online:2007-12-24
  • Contact: XU Fang-min

摘要:

In this article, a new approach for modeling multi- input multi-output (MIMO) systems with unknown nonlinear interference is introduced. The semiparametric theory based MIMO model is established, and Kernel estimation is applied to combat the nonlinear interference. Furthermore, we derive MIMO capacity for these systems and explore the asymptotic properties of the new channel matrix via theoretical analysis. The simulation results show that the semiparametric theory based modeling and kernel estimation are valid to combat this kind of interference.

关键词:

MIMO; partial linear model; kernel estimation; unknown nonlinear interference

Abstract:

In this article, a new approach for modeling multi- input multi-output (MIMO) systems with unknown nonlinear interference is introduced. The semiparametric theory based MIMO model is established, and Kernel estimation is applied to combat the nonlinear interference. Furthermore, we derive MIMO capacity for these systems and explore the asymptotic properties of the new channel matrix via theoretical analysis. The simulation results show that the semiparametric theory based modeling and kernel estimation are valid to combat this kind of interference.

Key words:

MIMO; partial linear model; kernel estimation; unknown nonlinear interference