摘要
针对分布式多天线信道随机时变特征参数的获取问题,通过参数化建模方法建立信道时变参数的自回归模型,将由频率偏置和复信道衰落构成的强非线性观测方程在估计值处展开成泰勒级数进而线性化观测方程后,运用扩展卡尔曼滤波算法联合估计未知参数。仿真结果表明,该方法可在序贯的观测值下对信道时变参数进行联合估计和跟踪,能获得逼近克拉默—拉奥下界的估计精度。
In order to estimate random time-varying wireless channel characteristic parameters of distributed multi-input multi-output （MIMO） communication systems, autoregressive models of unknown parameters were established according to statistical models of MIMO channel. By linearization of observation equation composed of multiple frequency offset and complex channel fading at estimate values, it applied extended Kalman filter to jointly obtain the parameters. Simula- tion results show that the method can estimate and track time-varying parameters under sequential observation. It can ob- tain the estimate accuracy close to Cramer-Rao lower bound.
出处
《通信学报》
EI
CSCD
北大核心
2013年第2期186-190,共5页
Journal on Communications
基金
国家自然科学基金资助项目(61101209)
上海市自然科学基金资助项目(11ZR1426600)
上海师范大学重点学科基金资助项目(DZL126)~~
关键词
分布式多天线
无线信道
信道状态信息
扩展卡尔曼滤波
非线性滤波
distributed multi-antenna
wireless channel
channel state information
extended Kalman filter
nonlinear filter
