CAJ | 학술논문

该文推导了多输入多输出(MIMO)系统中的符号定时、频偏和信道参数的联合最大似然(ML)估计.针对联合ML估计没有闭合的表达式、数值计算复杂度高的问题,该文提出了一种基于重复结构的正交训练序列的简化估计算法.该估计算法形式简单、复杂度低,且仍为最大似然估计.最后仿真分析了最大似然参数估计的均方误差与接收信噪比和天线数目的关系,并与Cramer-Rao界作了比较,表明了该算法的有效性.
해문추도료다수입다수출(MIMO)계통중적부호정시、빈편화신도삼수적연합최대사연(ML)고계.침대연합ML고계몰유폐합적표체식、수치계산복잡도고적문제,해문제출료일충기우중복결구적정교훈련서렬적간화고계산법.해고계산법형식간단、복잡도저,차잉위최대사연고계.최후방진분석료최대사연삼수고계적균방오차여접수신조비화천선수목적관계,병여Cramer-Rao계작료비교,표명료해산법적유효성.
This paper addresses the issue of joint Maximum-Likelihood (ML) time-frequency synchronization and channel estimation for Multiple-Input Multiple-Output (MIMO) systems. The resulting joint ML estimation requires solving a maximization problem with no closed-form solution. Since numerical calculation of the estimation is computationally hard, a computationally efficient closed-form ML solution is proposed using two repetitions of orthogonal training sequences. With theoretical analysis and simulations, the mean-square errors of the ML estimates versus the average SNR and the number of antennas are investigated, and then the performance of the proposed estimator is compared with the Cramer-Rao Bound (CRB). The results prove the effectiveness of proposed estimator.