## 3 MIMO System Model and the Implementation Steps of Channel Estimation  
### MIMO System Model  
In the MIMO system, the signal is transmitted through more than one antenna and received on multiple antennas. It consists of a number of antennas connected in both the transmitter and receiver side that is represented as \(A_{z}\) and \(A_{x}\), correspondingly. Trigger in the signal can be reduced by using the subcarrier \(S_{er}\) signal. The input data from the transmitter antenna is denoted as \(Z^{th}\) and is transferred to the receiver side antenna in \(S\times 1\) vector. The cyclic prefix function is performed with the aid of the Fast Fourier Transform (FFT) based on the length of the vector and it is denoted by \(L_{1}\). The vector length varies based on the channel size, which is given by \(L_{1}\geq N_{1}-1\). Here, \(N_{1}\) denotes the total size \begin{table}
\begin{tabular}{c c c c} \hline
**Author [Citation]** & **Methodology** & **Features** & **Challenges** \\ \hline \multirow{3}{*}{[PERSON] et al. [1]} & \multirow{3}{*}{Blind channel estimation technique} & Reduces the overhead loss and BER in the channel. & Energy utilization is a little high. \\  & & Decreases the carriage lag. & Life span is less when compared to other networks. \\ \hline \multirow{3}{*}{[PERSON] et al. [2]} & \multirow{3}{*}{DCNN} & Productively decreases packet loss and enhances network flexibility. & Does not provide independent outcomes. \\  & & During the transmission of data in a wireless network, dead nodes are easily identified. & Computation cost is high. \\ \hline \multirow{3}{*}{[PERSON] et al. [3]} & \multirow{3}{*}{WCT} & Gives high production. & Reduces the quality of the network. \\  & & Provides a stable network lifetime. & BER is high. \\ \hline \multirow{3}{*}{[PERSON] et al. [4]} & \multirow{3}{*}{GAN} & Long-range communications are possible in this wireless network. & Energy consumption is very high. \\  & & Generates artificial data that are very similar to real data. & Implementation cost is high. \\ \hline \multirow{3}{*}{[PERSON] et al. [5]} & \multirow{3}{*}{BP} & Path dependability in the network is low. & Does not contain a fault tolerance mechanism. \\  & & Total data communication time is low. & Amplitude information is completely lost in the network. \\ \hline \multirow{3}{*}{[PERSON] et al. [6]} & \multirow{3}{*}{RNN and CNN} & The transmission distance is reduced to improve communication in the wireless network. & Long sequences of data are difficult to process. \\  & & Packet information dropping is reduced using the RNN network. & Data loss and data traffic are high in this network. \\ \hline \multirow{3}{*}{[PERSON] et al. [7]} & \multirow{3}{*}{FCNN} & Avoids the data load and reduces the energy value of sensors. & Data security is one of the challenging tasks. \\  & & The memory size is acceptable, so it prevents the packet of information loss in the network. & Small-area implementation is not possible in this model. \\ \hline \multirow{3}{*}{[PERSON], et al. [8]} & \multirow{3}{*}{TNN and DNN} & Power consumption is less and reduces the collision in the network. & Network probability is high. \\  & & The quality of the network is high. & Suffers from overfitting problems. \\ \hline \end{tabular}
\end{table} 
Table 1: Features and Challenges of the Prior Channel Estimation Techniques in MIMO Systems. of the channels in the MIMO system. The cyclic prefix function is represented by \(V^{y}(w)\) and it is evaluated using the formula provided in Equation (1). \[V^{y}(w)=\sum_{z=1}^{q_{t}}E^{y,z}_{y_{w}}\Gamma^{e}F^{y}(w)+\phi(w) \tag{1}\]  
In wireless networks, MIMO has the capacity to improve the channel throughput with multiple numbers of antennas. MIMO systems improve the bandwidth of data without any transmission power. Thus, the authenticity of the system is high. The MIMO antennas operate at the same frequency by reducing the BER to improve the capability of the channel.