This information describes how a signal propagates from the transmitter to the receiver and represents the combined effect of, for example, scattering, fading, and power decay with distance.
The CSI makes it possible to adapt transmissions to current channel conditions, which is crucial for achieving reliable communication with high data rates in multiantenna systems.
CSI needs to be estimated at the receiver and usually quantized and feedback to the transmitter (although reverse-link estimation is possible in time-division duplex (TDD) systems).
Instantaneous CSI (or short-term CSI) means that the current channel conditions are known, which can be viewed as knowing the impulse response of a digital filter.
This gives an opportunity to adapt the transmitted signal to the impulse response and thereby optimize the received signal for spatial multiplexing or to achieve low bit error rates.
This description can include, for example, the type of fading distribution, the average channel gain, the line-of-sight component, and the spatial correlation.
As with instantaneous CSI, this information can be used for transmission optimization.
The CSI acquisition is practically limited by how fast the channel conditions are changing.
In fast fading systems where channel conditions vary rapidly under the transmission of a single information symbol, only statistical CSI is reasonable.
On the other hand, in slow fading systems instantaneous CSI can be estimated with reasonable accuracy and used for transmission adaptation for some time before being outdated.
In a narrowband flat-fading channel with multiple transmit and receive antennas (MIMO), the system is modeled as[1] where
, as multivariate random variables are usually defined as vectors.
Since the channel conditions vary, instantaneous CSI needs to be estimated on a short-term basis.
is estimated using the combined knowledge of the transmitted and received signal.
is transmitted over the channel as By combining the received training signals
The estimation mean squared error (MSE) is proportional to where
is equal to (or larger than) the number of transmit antennas.
The simplest example of an optimal training matrix is to select
as a (scaled) identity matrix of the same size that the number of transmit antennas.
If the channel and noise distributions are known, then this a priori information can be exploited to decrease the estimation error.
denotes the Kronecker product and the identity matrix
The estimation MSE is and is minimized by a training matrix
But there exist heuristic solutions with good performance based on waterfilling.
It needs however additionally the knowledge of the channel correlation matrix
In absence of an accurate knowledge of these correlation matrices, robust choices need to be made to avoid MSE degradation.
[5][6] With the advances of deep learning there has been work [7] that shows that the channel state information can be estimated using Neural network such as 2D/3D CNN and obtain better performance with less pilot signals.
The main idea is that the neural network can do a good interpolation in time and frequency.
[8] In a blind approach, the estimation is based only on the received data, without any known transmitted sequence.
A data-aided approach requires more bandwidth or it has a higher overhead than a blind approach, but it can achieve a better channel estimation accuracy than a blind estimator.