## 4 Results and Discussions
### Performance Analysis
#### 4.1.2 Mean Square Error  
It is calculated as the average squared deviations between estimated and actual values. Its risk function is equivalent to the predicted values of error function loss.  
A comparison of the simulation and analytical results, which yield the M.S.E. and BER of equalization generated by DFT-based then Wiener channel estimators, is shown in Tables 2 and 3 and Figure 3. The output of the simulation's MSE-related equalizer is derived using analytical and semi-analytical methods. A minor disagreement was found with high SNR (>15 dB) intended for a DFT-based channel estimator.  
\begin{table}
\begin{tabular}{c c} \hline No FFT Points & 1085 \\ \hline Length of Cyclic Prefix & 648 \\ \hline Total no of Subcarriers & 5385 \\ \hline Total no of Symbol & 289 \\ \hline Pilot Arrangement & Recursive type \\ \hline Pilot Constellation & QPSK \\ \hline Data Constellation & QPSK \\ \hline Bandwidth & 9 MHZ \\ \hline SNR Range & 0–30 dB \\ \hline Data subcarrier & 64, 128, 256, 512 \\ \hline Pilot spacing & 16, 32 \\ \hline Number of Iterations & 300 \\ \hline Denoising model & Wiener filter \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S. No** & **Authors Name** & **Methods** & **Merits** & **Demerits** \\ \hline \multirow{3}{*}{1.} & \multirow{3}{*}{[PERSON], [PERSON] et al. [7]} & Hybrid mmWave & It yields substantial system throughput gains with reduced computational overhead & Necessary to reduce SNRs when huge numbers of the antenna are used \\  & & & \\ \cline{3-4}  & & & \\ \hline \multirow{3}{*}{2.} & \multirow{3}{*}{[PERSON], [PERSON] et al. [8]} & Bit and power allocation algorithm & Reduced Bit Error Rate & Still, it faces problems with low complexity bit and a power allotment \\ \hline \multirow{3}{*}{3.} & \multirow{3}{*}{[PERSON], [PERSON] et al. [10]} & New Adaptive greedy algorithm strategy & Reduced M.S.E. & Superior techniques are necessary to boost power allocation with reduced complexity \\ \hline \multirow{3}{*}{4.} & \multirow{3}{*}{[PERSON], et al. [41]} & Spatial partitioning with coalitional game theory (SPCGT) & Improved power allocation & The convergence rate has to be improved \\ \hline \multirow{3}{*}{5.} & \multirow{3}{*}{[PERSON] et al. [31]} & Energy-efficient equal power allocation method using G.A. & Optimal performance with minimal complexity & M.S.E. is a concern \\ \hline \multirow{3}{*}{6.} & \multirow{3}{*}{[PERSON], [PERSON] et al. [42]} & Channel Prediction based Temporal Multiple Sparse Bayesian Learning for Channel Estimation & Considerably increased the throughput & It faces problems with local optimum \\ \hline \multirow{3}{*}{7.} & \multirow{3}{*}{[PERSON], [PERSON] et al. [49]} & Singular value decomposition & Improved diversity gains & Advanced techniques are necessary to yield optimal power allocation \\ \hline \hline \end{tabular}
\end{table}
Table 2: Analysis of optimal power allocation methods.  
Figure 3: MSE and BER for DFT-LS-WIENER.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**SNR (dB)** & **BER** & **MSE** \\ \hline
0 & 0.088385 & 0.015678 \\
3 & 0.034779 & 0.007881 \\
6 & 0.010104 & 0.003916 \\
9 & 0.002036 & 0.001961 \\
12 & 0.000367 & 0.000987 \\
15 & 5.47 \(\times\) 10\({}^{-5}\) & 0.000494 \\ \hline \hline \end{tabular}
\end{table}
Table 3: MSE and BER of hybrid DFT-LS-WIENER.