屠佳, 蔡跃明. 同道干扰下放大转发协同OFDM系统中的信道估计[J]. 信号处理, 2011, 27(3): 346-356.
引用本文: 屠佳, 蔡跃明. 同道干扰下放大转发协同OFDM系统中的信道估计[J]. 信号处理, 2011, 27(3): 346-356.
TU Jia, CAI Yue-Ming. Channel Estimation for an OFDM-Based Amplify-and-Forward Relay  System in the Presence of Co-channel Interferences[J]. JOURNAL OF SIGNAL PROCESSING, 2011, 27(3): 346-356.
Citation: TU Jia, CAI Yue-Ming. Channel Estimation for an OFDM-Based Amplify-and-Forward Relay  System in the Presence of Co-channel Interferences[J]. JOURNAL OF SIGNAL PROCESSING, 2011, 27(3): 346-356.

同道干扰下放大转发协同OFDM系统中的信道估计

Channel Estimation for an OFDM-Based Amplify-and-Forward Relay  System in the Presence of Co-channel Interferences

  • 摘要: 该文针对放大转发(Amplify-and-forward, AF)协同OFDM系统,在导频和数据子载波都受到多个严重的同道干扰(Co-channel Interference, CCI)影响的前提下,提出了一种基于导频辅助的迭代信道估计方法,并推导了信道估计的CramérRao界(Cramer-Rao Bound, CRB)。将等效信道、干扰信道和直传信道的信道状态信息(Channel State Information,CSI)估计值以及等效噪声方差矩阵的估计值进行线性合并,进一步消除CCI产生的影响,从而有效地提高了接收机的检测性能。理论分析和仿真结果表明:对于AF协同OFDM系统,在有多个同道干扰存在的情况下,本文提出的方法能够有效估计出所有等效信道和干扰信道的信道状态信息以及等效噪声方差矩阵,可以获得较好的均方误差(Mean Square Error,MSE)性能,且运算复杂度远远低于传统的线性最小均方误差(Linear Minimum Mean Square Error,LMMSE)信道估计方法。

     

    Abstract: In this paper, we consider an OFDM-based amplify-and-forward (AF) relay system in the presence of multiple co-channel interferences (CCIs). On the premise of severe CCIs in both the data and pilot subcarriers, we propose an iterative approach for the pilot-aided channel estimation and derive the CramerRao Bound (CRB) of the channel estimation. The estimation values of all channel state information (CSI) and the equivalent noise covariance are combined linearly at the receiver to mitigate the degradation which comes from the CCIs, so the detection performance of the receiver can be improved effectively. Simulation results show that when multiple CCIs exist in the OFDM-based AF relay system, our proposed channel estimation method can effectively estimate all CSI of the equivalent channel and the interference channels and the equivalent noise variance, and its mean square error (MSE) performance is as good as the traditional linear minimum mean square error (LMMSE) channel estimation, with the advantage of the lower complexity.

     

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