# Enhancing PAPR and Throughput for DFT-s-OFDM System Using FTN and IOTA Filtering
## 4 Simulation Results and Analysis
### Papr  
In this section, PAPR performance is compared within different MCSs, each with different values of acceleration factor \(\alpha\). Figures 4-6 show the PAPR performance based on QPSK under various MCSs. From the figures, we can conclude that no matter what filter is used (i.e., IOTA or RRC), the PAPR of FTN first decreases and then increases with the decrease of the acceleration factor \(\alpha\), and the PAPR of FTN reaches the optimal value when the acceleration factor is about 0.75-0.85. For higher modulation orders, higher spectral efficiency can be expected, and thus higher PAPR values can be seen. However, PAPR performance has little relation with the coding rate \(R\) when modulation orders are the same. \begin{table}
\begin{tabular}{c c} \hline Carrier Frequency & 70 GHz [33] \\ \hline Subcarrier spacing (SCS) & \(\Delta f=960\) kHz \\ \hline Symbol Interval & \(T=1.04\)\(\mu\)s \\ \hline System bandwidth & 800 MHz \\ \hline DFT size & 7792 \\ \hline Guard band & 40 MHz \\ \hline Allocated subcarriers & \(M=792\) (DFT size) \\ \hline FFT size & 1024 \\ \hline Roll-off factor of RRC & \(\beta=1/9\) \\ \hline RRC taps & \(20(\alpha=1)/28(\alpha=0.95)/30(\alpha=0.9)/\) \\ \(32(\alpha=0.83)/34(\alpha=0.77)/42(\alpha=0.64)/\) \\ \(52(\alpha=0.5)/68(\alpha=0.38)/102(\alpha=0.25)\) \\ \hline Up-sampling factor for signal & \(K^{\prime}=\alpha K_{0}(K_{0}=78)\) \\ \hline Channel & AWGN \\ \hline Channel coding & LDPC \\ \hline Modulation & QPSK, 16 QAM, 64 QAM \\ \hline Baseline Waveforms & DFT-s-OFDM/NOW [32] \\ \hline \end{tabular}
\end{table} 
Table 2: Parameters for Simulation. Figure 4: Performance of PAPR based on QPSK with \(R=120/1024\). Besides, in the simulation process we have, in total, nine MCSs, each with nine indexes (i.e., we have 81 cases actually). The corresponding modulation order, coding rate and spectral efficiency are listed in Table 3, including the corresponding value of \(\alpha\) with experimentally best PAPR performance and PAPR gains based on IOTA in each case. Denoted modulation order as Mod, coding rate as \(R\), spectral efficiency as SE, \(\alpha\) for best PAPR on IOTA as best \(\alpha\) -IOTA and \(\alpha\) for best PAPR on RRC as best \(\alpha\) -RRC. Figure 5: Performance of PAPR based on 16 QAM with \(R=490/1024\). Figure 6: Performance of PAPR based on 64 QAM with \(R=948/1024\). The reason why proposed method can reduce PAPR is analyzed in Figures 7-9. When the acceleration factor \(\alpha\) of FTN is large, the degree of compression is relatively low. With the increase of compression degree, the average value increases continuously. The increment of peak value mainly depends on the size of the tails of the pulse. When FTN is further compressed, the size of the peak part is mainly determined by the central envelope of other pulses. In this case, the peak value increases sharply with the increase of the compression degree. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**MCS** & **Mod** & **R** & **Best \(\alpha\)** & **Best \(\alpha\)** & **PAPR Gain** & **PAPR Gain** & **PAPR Gain** \\
**Index** & **Order** & **[/1024]** & **SE** & **-IOTA** & **-RRC** & **over RRC /dB** & **over DFT /dB** \\ \hline
0 & 2 & 120 & 0.2344 & 0.77 & 0.77 & 2 & 3.5 \\ \hline
1 & 2 & 379 & 0.7402 & 0.77 & 0.77 & 2 & 3.5 \\ \hline
2 & 2 & 679 & 1.3262 & 0.77 & 0.77 & 1.8 & 3.5 \\ \hline
3 & 4 & 340 & 1.3281 & 0.83/0.9 & 0.77 & 1.6 & 2.6 \\ \hline
4 & 4 & 490 & 1.9141 & 0.83 & 0.77 & 1.3 & 2.4 \\ \hline
5 & 4 & 658 & 2.5703 & 0.83 & 0.83/0.77 & 1.5 & 2.5 \\ \hline
6 & 6 & 438 & 2.5664 & 0.83 & 0.83/0.77 & 0.9 & 1.9 \\ \hline
7 & 6 & 719 & 4.2129 & 0.83 & 0.83 & 1.3 & 1.7 \\ \hline
8 & 6 & 948 & 5.5547 & 0.83 & 0.83 & 1.1 & 1.8 \\ \hline \end{tabular}
\end{table} 
Table 3: PAPR gains under different MCS cases. Figure 7: Peak value of RRC filter-based signal with \(\alpha=1\).