基于整体最小二乘的联合信道估计及OFDM信号检测算法
发布时间：2019-07-31 14:07
【摘要】：针对现有的联合信道估计及OFDM信号检测算法性能不高的问题,该文提出一种基于整体最小二乘的联合信道估计及OFDM信号检测算法。该算法首先通过导频估计初始的信道信息,在此基础上不断地采用整体最小二乘进行OFDM信号检测及信道估计,有效缓解迭代模型误差的影响,加快了算法迭代的收敛速度,提高了信道估计的精度,从而降低了OFDM系统的误码率。该文推导的信道估计克拉美罗界及仿真结果均表明所提出的算法在时变信道环境下优于现有的联合信道估计及OFDM信号检测算法。
[Abstract]:In order to solve the problem that the existing joint channel estimation and OFDM signal detection algorithms are not high, a joint channel estimation and OFDM signal detection algorithm based on global least square is proposed in this paper. Firstly, the initial channel information is estimated by pilot, on the basis of which the global least square is used for OFDM signal detection and channel estimation, which effectively alleviates the influence of iterative model error, accelerates the convergence speed of algorithm iteration, improves the accuracy of channel estimation, and thus reduces the bit error rate of OFDM system. The proposed algorithm is superior to the existing joint channel estimation and OFDM signal detection algorithms in time-varying channel environment.
【作者单位】： 西安电子科技大学综合业务网理论及关键技术国家重点实验室;
【基金】：国家自然科学基金(61271299) 高等学校学科创新引智计划项目(B08038)资助课题
【分类号】：TN911.23
[Abstract]:In order to solve the problem that the existing joint channel estimation and OFDM signal detection algorithms are not high, a joint channel estimation and OFDM signal detection algorithm based on global least square is proposed in this paper. Firstly, the initial channel information is estimated by pilot, on the basis of which the global least square is used for OFDM signal detection and channel estimation, which effectively alleviates the influence of iterative model error, accelerates the convergence speed of algorithm iteration, improves the accuracy of channel estimation, and thus reduces the bit error rate of OFDM system. The proposed algorithm is superior to the existing joint channel estimation and OFDM signal detection algorithms in time-varying channel environment.
【作者单位】： 西安电子科技大学综合业务网理论及关键技术国家重点实验室;
【基金】：国家自然科学基金(61271299) 高等学校学科创新引智计划项目(B08038)资助课题
【分类号】：TN911.23
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