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For each transmit antenna, we use a training sequence comprised of M long-training OFDM symbols.
For each transmit antenna, we use a training sequence comprised of M long-training OFDM symbols Data transmission stage: users transmit data frames comprised of N OFDM symbols according to different transmission schemes.
Schmidl and Cox in [14] proposed to use a training sequence that is composed of two symbols for time and frequency synchronization.
Normalised least mean squares (NLMS) based adaptive crosstalk cancellers (Gujrathi et al. (2009) [1]) have a low computational overhead but use a training sequence to ensure they converge adequately.
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MP applies sequential forward selection to determine a sparse representation of the channel h by using a training sequence and its corresponding received signal.
In [24, 25], the authors considered an imperfect CSI model with estimation errors in channel parameters, and they used a training sequence to estimate the channel.
The fading estimation is independently performed on each block of symbols (using a training sequence transmitted at the beginning of each block).
MP and OMP, two greedy algorithms originally proposed for sparse recovery problems, sequentially estimate the sparse channel by using a training sequence [14, 28].
There are plenty of real time adaptive channel identification algorithms [18] which provide fast and simple channel state information by using a training sequence where is a transmitted binary sequence known at the receiver and is the corresponding received sequence.
However, using a training sequence consumes some amount of bandwidth which can be avoided if an unsupervised or blind algorithm like a normalised multi-modulus algorithm (NMMA) is used instead.
In [22], the authors proposed an iterative channel estimator based on the EM algorithm to separately estimate the channels B → R (BS-RN) and R → U (RN-UT), that on the initial phase uses a training sequence and after can use the regenerated data.
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