数据辅助的时域自相关与互相关联合频偏估计方法

Data-aided time-domain joint auto-correlation and cross-correlation frequency offset estimation method

针对数据辅助下突发数据通信中频偏估计精度低和复杂度高的问题,提出了一种数据辅助的时域自相关与互相关联合频偏估计方法。首先,推导出基于通用数据帧结构的频偏估计克拉美劳界(CRB),同时引入一个形式上更为简单的近似CRB作为估计算法的性能界;然后,在自相关估计中,利用自相关算子和复信号指数化近似得到具有较大范围和较低信噪比门限的自相关算法;在互相关估计中,借鉴自相关估计原理,利用互相关算子获得兼顾低复杂度和高精度的互相关算法。仿真结果表明,所提方法可估计出接近符号速率一半的载波频偏且达到了近似CRB性能;与经典的M&M算法相比,所提方法的估计精度提高了5倍,且从实乘运算来看还具有与导频长度相关的线性复杂度,适用于突发数据通信的工程应用。

Considering the problems of low accuracy and high complexity of frequency offset estimation of data-aided burst data communications, a data-aided time-domain joint auto-correlation and cross-correlation frequency offset estimation method was proposed. Firstly, a general data frame structure based frequency offset estimation Cramer-Rao Bound(CRB) was derived, and a CRB with simpler form was introduced as the performance bound of the estimation algorithm. Then, in the auto-correlation frequency offset estimation, a auto-correlation algorithm with large range and low signal-to-noise ratio threshold was obtained using the auto-correlation operator and the exponent approximation of a complex signal;in the cross-correlation frequency offset estimation, a cross-correlation algorithm with low complexity and high accuracy was obtained by means of the cross-correlation operator and the principle of auto-correlation estimation. The simulation results show that, the proposed method can estimate the carrier frequency offset as large as half of the symbol rate with a near CBR performance, and compared to the classic M&M(Mengali & Moerlli) algorithm, its estimation accuracy is improved by five times and it has linear complexity related to the pilot length according to real multiplication operations, which is suitable for the engineering applications of burst data communications.