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The users feed back limited CSI (quantized CSI) which can be corrupted by errors introduced by the feedback channel.
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Establishing a model that is capable of accurately predicting the concentrations of multiple components from infrared absorbance measurements that may be corrupted by error requires a carefully designed calibration procedure a key part of which is model regression.
Now assume that the coded packets arranged in are transmitted and they are corrupted by errors.
The measurements are corrupted by errors which are mainly due to quantization but also other sources of noise exist.
When the sensors are subject to perturbations due to errors in calibration or in sensor location, the signal response is corrupted by errors characterized by the vectors { Δ a i } i = 1 K added to the steering vectors { a ( θ i ) } i = 1 K. Theses errors are randomly changing from one observation period to another.
with the θ i being the true DOA; however, the measured samples are corrupted by error due to the spatial spread of the multipath component and impairments in measurement.
It should be noted that for schemes using MRT, the channel estimation errors will produce a twofold effect: first, the beamforming weights will be corrupted by these errors, and second, the coherent demodulation will also be affected.
Thus their memory was more prone to be corrupted by misbinding errors in VSTM: failures to correctly bind features (colour and orientation) of encoded items in memory (Figs 3C and 4B).
Based on the above discussion, the probability that a data frame will be corrupted by bit-errors during transmission is.
In vivo MRI data can be corrupted by motion.
Also, the signal could be corrupted by the noise.
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