Sentence examples for using a training sequence from inspiring English sources

Exact(7)

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].

MP applies sequential forward selection to determine a sparse representation of the channel h by using a training sequence and its corresponding received signal.

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.

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.

While at time slot t + 1, the distributed jamming beamforming weight is stacked in vector f d  = [f d1, f d2, ⋯, f d(M − 1)] T. Regarding the available CSI in a wireless communication system, the receiver usually estimates the channel using a training sequence (pilot symbols).

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Similar(53)

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.

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.

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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