基于序贯蒙特卡罗滤波的MIMO快时变信道跟踪

高数据传输速率以及终端的高速移动,导致无线通信信道具有时间选择性与频率选择性两个特征.本文主要研究了基于训练序列的多输入多输出（MIMO）时变频率选择性衰落信道的估计与跟踪问题.首先,根据时变无线信道的动态性,将信道冲击响应近似看作一个低阶的自回归矢量过程（AR）,以便于进行时变信道的跟踪.接着在此模型的基础上,利用序贯蒙特卡罗滤波对MIMO通信系统中的双选择性信道进行了跟踪;跟踪过程中需要与信号检测交替进行,即在状态变量的预测和新息修正的中间要进行一次码元的检测,所采用的方法是极大似然序列检测,最后与扩展卡尔曼滤波作了比较.仿真结果表明,在信道噪声是非高斯的情形下,序贯蒙特卡罗滤波的跟踪性能更优越于扩展卡尔曼滤波.

Time-varying channel tracking using sequential Monte Carlo filter in MIMO systems

High data transmission rates and high mobility give rise to time- and frequency- selectivity in wireless communication channels. The problem of channel estimation and tracking for MIMO time-varying frequency-selective channels is investigated based on training sequences. Relying on the time evolution of time-varying wireless channel, a low order autoregressive vector model approximates the MIMO channel variation and facilitates time-varying channel tracking, Then, this paper proposes the use of Sequential Monte Carlo Filter for tracking MIMO doubly selective channel, and employs MLSE schemes for symbol detection, finally compares with extended Kalman filter. Simulation results show that the proposed tracking method offers better tracking performance than extended Kalman filter algorithm.