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| Joint ML Time-Frequency Synchronization and Channel Estimation for MIMO Systems Using Orthogonal Training Sequences |
| Pei Min-yan; Cheng Wen-jing; Wei ji-bo |
| School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China |
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Abstract 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.
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Received: 12 February 2009
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Corresponding Authors:
Pei Min-yan
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2010, Vol. 32 
