Abstract
The EM algorithm is a general method for ML channel estimation that arise in statistical estimation problems. However, this algorithm has two drawbacks which are slow convergence and large complexity. In order to reduce the complexity of the EM algorithm, we adapt quasi-Newton methods to the EM algorithm for channel estimation in frequency selective environments. The proposed methods and conventional EM algorithm are compared with each other in terms of complexity and the BER performance. The performance evaluations are based on the European digital television system DVB-H. Also, the outstanding performance of this proposed methods is shown and compared with other existing methods in DVB-H.
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