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The channel impulse response (CIR) can be estimated by channel estimation methods such as zero forcing and MMSE.
There are some channel estimation methods for this part such as LS and LMMSE estimator [4].
The Bayesian learning channel estimation methods have been developed to reconstruct the sparse channel.
The efficient timing and channel estimation methods for our system can be found in [26 28].
We now consider μ XY and μ XX which depend on channel estimation methods.
Section 3 describes the 2-D and DD channel estimation methods.
The proposed different channel estimation methods are applied according to the kind of channel model.
The following are the improved adaptive sparse channel estimation methods: ZA-NLMS (proposed).
The present channel estimation methods generally can be divided into two kinds.
Figure 5 BER performance versus the SNR with different FD channel estimation methods.
Figure 3 MSE performance versus the SNR with different FD channel estimation methods.
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