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In the first one, the derived optimal training conditions were respected.
have been proposed: The sequences (32) satisfy the optimal training conditions (24) and (25) if N > 2 L ~ max.
We have derived the conditions for optimal training sequences minimizing the MSE of the LMMSE estimator, which is found out to be equivalent to the optimal training conditions of the LS estimator.
In Appendix 2, we have provided a derivation of the LS estimator and its optimal training condition to minimize the MSE, where we have also shown the equivalence of the optimal training conditions for the LMMSE and LS estimators.
Under the power constraint ∥x i ∥2=N P i for i=1,2, the optimal training conditions of the LS estimator thus becomes the same as those of the LMMSE estimator given in (41), and the resultant minimum MSE of the LS estimator is given by MSE o t ̆ i = MSE o t ̆ i 1 + MSE o t ̆ i 2, (45).
Thirdly, although the general practitioners endorsed the software's high level of functionality under our optimal training conditions, it was more challenging in the field, reflecting both technological and end user difficulties that may have further compromised the time available for and the quality of the therapeutic interaction.
Similar(54)
The sequences in (34) satisfy the optimal training condition if N > 4 L ~ max.
It is verified that the optimal training condition for the LMMSE estimation is equivalent to that for the LS estimation.
In addition, we propose new sets of training sequences satisfying the optimal training condition and, at the same time, exhibiting the lowest PAPR.
We also propose new training sequences not only satisfying the optimal training condition but also providing the minimum peak-to-average power ratio.
In addition, we can independently design polyphase [23, 24] training sequences satisfying the optimal training condition based on the Chu sequence [25].
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