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These observations essentially show that the optimal training designs for the MVU and MMSE estimators in the classical channel estimation setup are nearly optimal for the application of MMSE channel equalization.
The authors of [17] presented training designs for estimation of spatially correlated MIMO AF two-way multi-relay channels, where an optimal training structure is initially derived to minimize total mean-square-error (MSE) of the channel estimation.
n T = 6, n R = 6, B = 8, a = 99. Figure 3 L-optimality criterion with arbitrary but positive semidefinite W 1, W 2 for the MMSE estimator with R R = S R. n T = 3, n R = 3, B = 4, a = 99. Figure 4 demonstrates the performance of optimal training designs for the MMSE estimator in the context of MMSE channel equalization.
In the second phase, at the country level, the country's representative would be assisted by a team of resource persons in developing detailed training designs for each session and training the appropriate persons.
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Training Designed for the Needs of Industry.
Now consider the training design for the iteration phase.
Also in [10], optimal training design for MIMO-OFDM systems with non-uniform placement of pilot tones has been addressed.
In Figure 1, we assume that R R = S R, and we implement the corresponding optimal training design for each scheme.
In this article, a new training design for a BICM-MIMO system over a block-fading channel has been proposed.
A natural question is whether the optimal training design for the initialization phase using PPSAM scheme is still optimal for subsequent iterations of an iterative channel estimator.
In [12], an optimal training design for both least-squares estimators and LMMSE estimators is studied assuming cyclic delay diversity OFDM systems.
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