## 4 Results and Discussion
However, improving several parameters might negatively affect other parameters, for instance, improving the PAPR increase, the computational complexity and vice versa, as in [31] and [33]. Table 8 shows a comparison between our work with the benchmark of the related work based on this argument. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**N**} & \multirow{2}{*}{**B**} & \multirow{2}{*}{**\(\mathbf{\delta}\)**} & \multirow{2}{*}{**\(\mathbf{\mu}\)SLM**} & \multirow{2}{*}{**\(\mathbf{\alpha}\)SLM**} & \multicolumn{3}{c}{**PAPR Reduction (QAM)**} & \multicolumn{3}{c}{**CM Reduction (QAM)**} \\ \cline{5-12}  & & & & & **4** & **16** & **64** & **256** & **4** & **16** & **64** & **256** \\ \hline
512 & 4 & 2 & 9216 & 18432 & 1 & 1.25 & 1.25 & 1.2 & 0.5 & 0.5 & 1.2 & 0.5 \\
512 & 6 & 3 & 13824 & 27648 & 1 & 1.25 & 1.3 & 1.75 & 0.75 & 0.75 & 1.2 & 0.6 \\
512 & 8 & 3 & 18432 & 36864 & 1.25 & 1.47 & 1.5 & 1.9 & 0.75 & 0.8 & 1.2 & 0.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Calculation results of the computational complexity of the SLM-UFMC alongside the reduction in the PAPR and cm parameters in DB. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Reference**} & \multicolumn{3}{c}{**Method/Waveform**} & \multicolumn{3}{c}{**Remarks**} \\ \hline \hline \multirow{4}{*}{[PERSON] et al. [27]} & • & SLM & • & Reduce the computational complexity without sacrificing the PAPR; \\  & • & OFDM & • & Slightly improve the bit error rate (BER); \\  & & & • & Neglect the side information. \\ \hline \multirow{4}{*}{[PERSON] and [PERSON] [30]} & • & SLM & • & Reduce the computational complexity; \\  & • & OFDM & • & Limiting the transformed data rate (BER); \\  & & & • & Neglect the side information. \\ \hline \multirow{4}{*}{[PERSON] et al. [31]} & • & CRRV & • & Reduce the PAPR; \\  & • & SLM & • & Increase the computational complexity; \\  & • & OFDM & • & Neglect the side information. \\ \hline \multirow{4}{*}{[PERSON] and [PERSON] [33]} & • & OFDM & • & Reduce the computational complexity; \\  & • & SLM & • & Increase the PAPR; \\  & & & • & Neglect the side information. \\ \hline \multirow{4}{*}{[PERSON] et al. [35]} & • & SRC & • & Reduce the computational complexity; \\  & • & Multiple-input-multiple-output (MIMO)-OFDM & • & Reduce the PAPR; \\  & & & • & Neglect the side information; \\ \cline{1-1}  & & & • & Increase the BER. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparing our work with the benchmark. From the above table, we can observe that the popular waveform is OFDM, and the SLM has been applied to the OFDM to reduce the PAPR of this waveform. Subsequently, the UFMC is considered as a promising waveform for 5G technology because it has a lower latency and better BER than the OFDM, but it is more complex. Moreover, both OFDM and UFMC suffer from high PAPR. Additionally, the table shows that the SLM for the UFMC waveform has not been proposed and evaluated. Our approach combines SLM with UFMC (SLM-UFMC) and is able to reduce the PAPR, BER and CM, but it neglects the side information and computational complexity. Hence, this study indicates that SLM has better spectral efficiency and can be used to reduce the Inter-Carrier Interference (ICI) which is symmetrical with the improvement in the UFMC signals. Ultimately, the SLM-UFMC shows better performance and consumes lower power than the SLM-CP-OFDM and conventional UFMC. The limitation of this work is that it neglects the side information and computational complexity.