虚拟多天线OFDM系统中多频偏和信道联合最大似然估计算法研究

Study of multiple frequency-offset and channel joint ML estimation over virtual multi-antennas OFDM system

针对各个收发天线间都具有不同的频偏和信道参数的虚拟多天线、OFDM系统目的节点中数据辅助的多频偏和信道参数估计问题进行了研究。通过利用级数展开的方法进行矩阵求逆,提出了一种基于最大似然准则的高计算效率参数估计算法,从而能够同时对虚拟多天线OFDM系统中的频偏和信道等参数进行估计。给出了本算法估计结果的均方误差与CR下界的对比,同时还对训练序列的设计进行分析和仿真验证,仿真结果表明所设计的低复杂度算法性能上逼近CR下界,同时克服了现有算法的缺陷。

A pilot-aided joint estimation of the carrier frequency offsets（CFO） and channels of a virtual multi- antennas（VMA） orthogonal frequency-division multiple（OFDM） system was considered. For a VMA-OFDM system, each pair of transmit and receive antennas possibly having a different CFO and CIR（channel impulse response） parameters made the parameters joint estimation a challenge task. By utilizing the series expansion of the matrix inversion and maximum likelihood（ML） criterion, a computationally efficient estimator allowing for simultaneously estimating the unknown parameters was developed. The mean square-error（MSE） performance of the interested parameters for the proposed estimator was compared with the Cramer-Rao bound （CRB）. In addition, the pilot sequence design was investigated by analysis and simulations. Simulation results show that the proposed low-complexity estimator asymptotically achieves the CRB and overcomes the drawbacks of existing estimators.