## 4 Simulation Results
### Computational Complexity  
One of the main concerns when designing a PAPR reduction method is its effect on the performance of the multicarrier systems. PAPR reduction methods are known to increase the system's computational complexity; therefore, implementation becomes even more challenging. This section analyzes the computation complexity of the proposed method and compares it with those of the pure FBMC/OQAM signal, DFT spreading FBMC/OQAM, and modified PTS FBMC/OQAM. The computation complexity calculations mainly involve the total number of real multiplications (RMs) and real additions. For our analysis, we focused on the RMs at the transmitter side only, including IDFT operation, DFT spreading operation, PPN filtering, and phase factor optimization.  
As mentioned in the previous section, for the IDFT operation, LN IFFT points were performed. The number of multiplications related to the IDFT operation for complex input data equals \(\frac{LN}{2}log_{2}N+LN/2\)[12]. Multiplying two complex numbers results in four RMs and three additions; therefore, the total number of multiplications are 4 \(\times\)\(\left[\frac{LN}{2}log_{2}N+LN/2\right]\)[12]. The total number of IDFTs for the PTS operation is 2V because the partitioning is separately performed in the real and imaginary parts; therefore, the total number of multiplications is 8V \(\times\)\(\left[\frac{LN}{2}log_{2}N+LN/2\right]\) for the proposed method. For DFT spreading, the total number of multiplications is 4 \(\times\)\(\left[\text{N}/2\ log_{2}N\right]\). The PPN filtering in the proposed method is performed 2V \(\times\) 8 KN times for the real and imaginary parts. Finally, the phase factor searching for the proposed method was set to 8 \(\times\)\(\left[2^{o-1}N(V+1)\right]\)[19].  
Table 3 summarizes the main calculations and comparison between pure FBMC/OQAM, DFT-FBMC/OQAM, PTS FBMC/OQAM, and the proposed method. The simulation parameters are N = 64, L = 4, K = 4, W = 2 {1, -1}, V = 4. As presented in the table, the total number of RMs of the pure FBMC/OQAM is the least, whereas those of the proposed method are the highest. As mentioned previously, most PAPR reduction methods work well in reducing the PAPR. However, it comes with the cost of increasing the computation complexity, BER, and spectral efficiency degradation. The conventional PTS method is known for its high computational complexity, mainly when performing V-IFFT operations and phase searching. As the number of sub-blocks increases, the computational complexity  
Figure 6: PAPR (peak-to-average power ratio) comparison of several reduction methods for N = 256.  
increases significantly. In [21], the author proposed reducing the PAPR of OFDM/OQAM by dividing the FBMC signal into segments and applying the PTS reduction scheme on each segment separately. Although the computational complexity of this method is less than that of the original PTS method, the structure of the OFDM/OQAM is different; thus, it cannot be directly applied to FBMC/OQAM. In the proposed method, as mentioned earlier, all previous operations are performed for the real and imaginary parts; therefore, we conclude that the proposed method has the highest computational complexity.