## 5 Numerical Results  
In previous sections, both thee INLP approach and heuristic method based adaptive GA are investigated to enhance the energy and transmission efficiency of the TWDM-PON enabled reconfigurable fronthaul. We also take the load balancing in the wavelength dimensioning into consideration. In this section, numerical simulation results are provided and analyzed, to evaluate the performance of the proposed algorithms. We consider a TWDM-PON enabled C-RAN system with N = 32 RRUs, each served by a one-to-one corresponding ONU. And S = 8 wavelengths are assumed to be used in the fronthaul network, forwarding the compressed bitrate-variable CPRI signals [38]. Moreover, it is known that the tidal effect is clear during the day in smart homes of office and residential. We consider a business area, where the typical traffic variation during an operational day is shown in Figure 2[60]. In order to reduce the response time, predicted traffic is used in the proposed algorithm. Other parameters are shown in Table 1.  
Figure 2: Traffic variation during an operational day [55]. Figure 3 illustrates the heuristic method based adaptive GA's converging condition. The initial population is consisted of a group of distributed individuals, each representing a candidate solution for the joint optimization. The diversity of the candidate solutions is increasing with the growing number of initial individuals. However, more hardware resources are required in the implementation. So, we find a proper population size \(K=100\) by evaluating several population sizes. It is clear that when we take the degree of diversity \(Dp=0.12\), and assume that the size of population \(K=100\), the GA achieves the convergence within 65 iterations. The running time of the adaptive GA is about 2.74 s, which is less than the traditional GA with fast convergence. Compared to the INLP approach, much lower running time is obtained by using the adaptive GA method on a computer with 3.2 GHz Intel Core 4 CPU and 4 GB RAM. This is because that the INLP method takes Million times iterations to achieve the global optimal solution. The adaptive GA is more efficient to find the near-optimal solution for the large-scale network optimization. Figure 4 presents the energy consumptions of TWDM-PON enabled reconfigurable fronthaul under three different conditions. Typical C-RAN with fixed topology and power supply consumes the most energy, since all the units including transceivers, ONUs, and RRUs are supposed to remain active, and supplied with energy all the time independent of traffic variation. C-RAN with INLP method obtains the global optimal solution for fronthaul energy minimization, leading to a significant energy saving. The active units of fronthaul network are dynamically turned off during appropriate times with the traffic fluctuation, whereas the INLP method is confronted with the lack of hardware resources. Moreover, C-RAN with the proposed adaptive GA approach achieves a better energy minimization solution than the C-RAN with fixed power supply. Compared to the INLP method, slightly less energy saving performance can be obtained using GA approach with lower complexity. By dynamically \begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & \\ \hline \(p_{Tt}\), per-active transceiver consumption & 5 W [61] \\ \(P_{ONU}\), per-active ONU consumption & 7.7 W [62] \\ \(P_{RRU}\), per-active RRU consumption & 20 W [20] \\ \(C_{W}\), per-wavelength maximum capacity & 10 Gbit/s [61] \\ \(\
u_{CPRI}\), per-CPRI link rate (option 3) & 2.5 Gbit/s \\ \(\mu_{i}\), compression ratio for each ONU & 0.2-1 \\ \(K\), constant population size for GA & 100 \\ \(\rho_{c}\), initial crossover probability & 0.8 \\ \(p_{m}\), initial mutation probability & 0.05 \\ \(D_{p}\), convergence threshold for GA & 0.12 \\ \((\alpha_{1},\alpha_{2},\alpha_{3})\) & (1/2, 3/8, 1/8) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation Parameters.