## 4 Discussion
### Performance Analysis
#### 4.2.2 Network Handover Analysis
This demonstrates that our algorithm maintains a good performance even under frequent optical path blockings. position, identifies whether the user will enter a blocking region, and avoids unnecessary handovers in these areas. When \(\lambda\) increases from 5 to 10 times per minute, the VHO rate increases from 0.53 to 0.62, a slight increase of 17%. At \(\lambda=10\) times per minute, the scheme proposed in this paper reduces the handover rate most significantly, reducing the horizontal and vertical handover rates by 56% and 53%, respectively, compared to STD-LTE. Compared with the FL-HO scheme, it reduces the handover rate by 35% and 17%, respectively. While our algorithm demonstrates effectiveness in reducing the handover rate under typical conditions, the method struggles to precisely determine user positioning and optimal network access points under severe indoor interference scenarios. This limitation manifests particularly in industrial environments with electromagnetic interference affecting Wi-Fi transmission, or when randomly positioned obstacles disrupt VLC line-of-sight links, potentially triggering excessive network handovers. Figure 11: Variation of the handover rate for different optical path-blocking scenarios. Figure 10: Variation of the handover rate for different users’ speed. (3) User fairness  
Figure 12 shows the variation of user fairness between different algorithms under different numbers of users. As can be seen from the figure, the fairness of STD-LTE decreases significantly with the increase in the number of users. When the VLC signal strength decreases, STD-LTE decides to handover based only on the signal strength, and users will handover to Wi-Fi for communication to pursue a better signal quality. However, the bandwidth resources of Wi-Fi APs are limited, and an increase in the number of user connections reduces the average available bandwidth per user, decreasing the average throughput. At the same time, VLC AP resources may remain underutilized, and the average throughput of users served by VLC APs may increase. This imbalance in resource allocation widens the throughput difference between users, leading to decreased fairness. The higher fairness of the scheme proposed in this paper and the FL-HO scheme is achieved because the FL-HO scheme constructs a centralized optimization problem, which takes the handover cost due to optical path blocking and user movement as the primary considerations. The scheme proposed in this paper can determine the user's movement trend and demand in advance, and the designed reward function introduces the handover cost, balancing parameter, encouraging the algorithm to select the AP that can satisfy the user's data rate demand and has low handover cost while guaranteeing the success of handover. For high-speed mobile users, the algorithm will tend to select Wi-Fi directly because the frequent handover to VLC may lead to the optical link blocking and low data rate, obtaining lower rewards; while for low-speed mobile users, direct HHO can obtain higher rewards, ensuring the fairness of the overall network. (4) Convergence case  
Figure 13 shows the reward convergence of the algorithm for different numbers of user connections, and the number of iterations required for the algorithm to converge increases gradually with the number of people. This is due to the rise in network complexity, increase in state space and action space, increase in diversity of different users' movement trajectories, data requirements, and optical link occlusion scenarios, resulting in the algorithm requiring more iterations to explore and learn from these complexities. Despite increasing users leading to slower convergence and lower reward values, the algorithm can gradually converge to a steady state, showing good robustness. Figure 12: Variation of user fairness of each scheme with different number of users.