首页> 外文期刊>Wireless personal communications: An Internaional Journal >Capacity Improvement for TDD-MIMO Systems via AR Modeling Based Linear Prediction
【24h】

Capacity Improvement for TDD-MIMO Systems via AR Modeling Based Linear Prediction

机译:通过基于AR建模的线性预测提高TDD-MIMO系统的容量

获取原文
获取原文并翻译 | 示例
           

摘要

The quality of channel state information (CSI) affects the performance of multiple input multiple output (MIMO) systems which employ multi-elements antenna arrays at both the transmitter and the receiver. In a time division duplex (TDD) systems, the CSI for downlink can be obtained from uplink channel using reciprocity principal. However, the performance of a MIMO system can be degraded due to channel impairments especially in fast fading scenarios when the CSI obtained from uplink is used for downlink transmission. In this paper, we study performance of autoregressive (AR) modeling based MIMO channel prediction under varying channel propagation conditions (mobile speed, multipath number and angle spread) and prediction filter order. Our simulation results show that using the predicted CSI for downlink provides capacity improvement compared to conventional method.
机译:信道状态信息(CSI)的质量会影响多输入多输出(MIMO)系统的性能,该系统在发送器和接收器上均采用多元素天线阵列。在时分双工(TDD)系统中,可以使用互惠性原理从上行链路信道获得下行链路的CSI。然而,由于将从信道上行链路获得的CSI用于下行链路传输的信道衰减,尤其是在快速衰落场景中,MIMO系统的性能可能由于信道损伤而降低。在本文中,我们研究了在变化的信道传播条件(移动速度,多径数和角度扩展)和预测滤波器阶数下,基于自回归(AR)建模的MIMO信道预测的性能。我们的仿真结果表明,与传统方法相比,将预测的CSI用于下行链路可提高容量。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号