Iterative Algorithms for Channel Identification Using Superimposed Pilots

Channel identification of a time-varying channel is considered using superimposed training. A sequence of known symbols with lower power is arithmetically added to the information symbols before modulation and transmission. The channel estimation is done exploiting the known superimposed data in the transmitted signal. Two iterative algorithms are considered in this paper: recursive least squares (RLS) and the expectation maximization (EM). Performance of the proposed algorithms is compared with a simple averaging scheme and the LMS algorithm. For short data blocks RLS outperforms EM, but with large blocks EM is superior

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