# Performance Evaluation of GFDM Channel Estimation Using DFT for Tactile Internet Application
## 3 Experimental Results
Next, the constellation of quadrature phase-shift keying (QPSK) is investigated, in order to compare LS and DFT-based channel estimation, as indicated in Figure 6. When channel estimation is not performed, the received signal cannot be recovered and the SER performance is very poor. When the channel estimation method is employed, the channel can be compensated and the data can be recovered. The DFT-based channel estimation method provides better variance than the LS channel estimation, as indicated by the parts that are more highly concentrated near the constellation point. Hence, the proposed method has better SER performance. Second, we discuss the performance of channel estimation under the NYUSIM channel. In an attempt to satisfy the tactile internet requirements, the parameters of channel dispersion should be calculated. Table 4 summarizes the dispersion parameters for each scenario. By referring to the requirements as given in Table 1, it can be concluded that all parameters meet the requirements for tactile internet application. Figure 5: GFDM performance evaluation in term of (**a**). MSE and (**b**). SER with 7% and 14% pilot symbol overhead. The performances of these three scenarios are observed in terms of SER and MSE, as shown in Figure 7. Overall, for all scenarios, the DFT-based estimation method provides a slight improvement of about 1.5 dB, compared to LS estimation methods, in terms of SER. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **Delay Spread (ns)** & **Bandwidth Coherence (MHz)** & **Doppler Frequency (Hz)** & **Time Coherence (ms)** \\ \hline Umi & 9.8 & 20,408 & 10 & 0.0423 \\ Uma & 32.1 & 6230 & 10 & 0.0423 \\ Rma & 0.2 & 1000 & 10 & 0.0423 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Dispersion Parameters of NYUSIM channel. Figure 6: QPSK demapper condition before and after channel compensation: (**a**) transmitted data; (**b**) without channel compensation; (**c**) LS channel estimation; (**d**) DFT channel estimation. These three scenarios are classified as frequency selective fading, since the received signal includes multiple versions of the transmitted signal. In the Umi scenario, although the number of channels is smaller than in the Uma scenario, the channel frequency response has a gain that is more varied for different frequency components. This occurs because the gain the power delay profile generated in Umi scenario is larger than in the Uma scenario. Therefore, the channel in the Umi scenario is more distorted than in the Uma scenario. This leads to better SER performance for the Uma scenario. This condition also effects to the Rma scenario, so that SER in this scenario is the best. In addition, for all scenarios, the MSE performance of the DFT-based estimation algorithm offers better results than the LS estimation algorithm. It can be also proven that the Uma scenario is better than the Uma scenario by observing the MSE performance. The fluctuation of the channel frequency response in the Umi scenario is faster than the Uma  
Figure 7: SER and MSE performance: (**a**) SER performance for Umi scenario; (**b**) SER performance for Uma scenario; (**c**) SER performance for Rma scenario; (**d**) MSE performance for all scenarios. scenario, so that the channel estimation algorithm cannot follow it. This leads to poor results in estimating the true channel. Furthermore, the Rma scenario provides linear MSE because the channel frequency response tends to be flat for different frequencies. Thus, the pilot symbol observation is more clear than in other scenarios.