Doubly selective channel estimation using superimposed training and exponential bases models
EURASIP Journal on Applied Signal Processing
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IEEE Transactions on Signal Processing
A low-complexity iterative channel estimation and detection technique for doubly selective channels
IEEE Transactions on Wireless Communications
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IEEE Transactions on Signal Processing
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In this paper, a new method to perform channel estimation is presented. It is shown that accurate estimation can be obtained when a training sequence is actually arithmetically added to the information data as opposed to being placed in a separate empty time slot: hence, the word "implicit." A closed-form solution for the estimation variance is derived, as well as the Cramer-Rao lower bound. Conditions are derived for the training sequences that result in a channel estimation performance that is independent of the channel characteristics. In addition, estimation performance is shown to be independent of the modulation format. A procedure to synthesize optimal training sequences is presented, and the problem of synchronization is solved. The performance of the algorithm is then compared with other methods that use explicit training under GSM-like environmental conditions, and the new algorithm is shown to be competitive with these. Finally, comparisons are also carried out against blind methods over realistic bandlimited channels, and these show that the new method exhibits good performance.