A Low-Complexity LMMSE Channel Estimation Method for OFDM-Based Cooperative Diversity Systems with Multiple Amplify-and-Forward Relays

A Low-Complexity LMMSE Channel Estimation Method for OFDM-Based Cooperative Diversity Systems with Multiple Amplify-and-Forward Relays
复制标题

DOI:
10.1155/2008/149803
复制
发表时间:
2008
影响因子:
2.6
通讯作者:
K. Yan;Sheng Ding;Yunzhou Qiu;Yingguan Wang;Haitao Liu
K. Yan;Sheng Ding;Yunzhou Qiu;Yingguan Wang;Haitao Liu
中科院分区:
计算机科学4区
文献类型:
--
作者:
K. Yan;Sheng Ding;Yunzhou Qiu;Yingguan Wang;Haitao Liu

文献摘要

被引文献

相似文献

基于正交频分复用(OFDM)的放大转发(AF)协作通信是单天线系统在频率选择性衰落信道中利用空间分集增益的有效方法,但接收机通常需要了解信道状态信息才能恢复传输信号。针对频率选择性衰落信道下基于OFDM的多AF中继协作分集系统,提出了一种训练序列辅助的线性最小均方误差(LMMSE)信道估计方法。推导了该方法的均方误差(MSE)界,并给出了关于此界的最优训练方案。通过利用最优训练方案,一个最优的低秩LMMSE信道估计器的引入,以减少所提出的方法通过奇异值分解的计算复杂度。此外,采用Chu序列作为训练序列,实现了最优训练方案,在源端易于实现,在中继端降低了计算复杂度。仿真结果验证了所提出的低复杂度信道估计方法的性能和所得到的最优训练方案的优越性。
Orthogonal frequency division multiplexing- (OFDM-) based amplify-and-forward (AF) cooperative communication is an effective way for single-antenna systems to exploit the spatial diversity gains in frequency-selective fading channels, but the receiver usually requires the knowledge of the channel state information to recover the transmitted signals. In this paper, a training-sequences-aided linear minimum mean square error (LMMSE) channel estimation method is proposed for OFDM-based cooperative diversity systems with multiple AF relays over frequency-selective fading channels. The mean square error (MSE) bound on the proposed method is derived and the optimal training scheme with respect to this bound is also given. By exploiting the optimal training scheme, an optimal low-rank LMMSE channel estimator is introduced to reduce the computational complexity of the proposed method via singular value decomposition. Furthermore, the Chu sequence is employed as the training sequence to implement the optimal training scheme with easy realization at the source terminal and reduced computational complexity at the relay terminals. The performance of the proposed low-complexity channel estimation method and the superiority of the derived optimal training scheme are verified through simulation results.