## 4 Discussion
### Performance Analysis
#### 4.2.2 Network Handover Analysis  
(1) Average throughput  
Figure 7 demonstrates the variation of system throughput with user speed. It can be seen that when the user speed is low (v < 1 m/s), the difference in throughput among the three handover methods is not apparent. As the user speed increases, the channel conditions change more frequently, leading to an increase in the number of handovers and a decrease in throughput. At normal walking speed (v = 2 m/s), the throughput is slightly higher than the two compared algorithms. This is because the method in this paper still maintains a specific exploratory ability while accumulating certain knowledge and can better adapt to the dynamically changing network environment. The handover mechanism of STD-LTE is based on a fixed threshold that triggers frequent handovers in the face of fast user movement, which reduces the adequate transmission time. The decision rule of the FL-HO scheme is fixed, and the decision-making is not precise enough in high-speed  
Figure 5: Comparison of the prediction error before and after improvement. Figure 6: Comparison of prediction accuracy at different repetition rates. movement. In contrast, the algorithm in this paper is based on user trajectory prediction and reinforcement learning, which can dynamically adjust the handover strategy according to the network state. When the user is moving at low speed or in a stationary state, the decision-making process is slower and the algorithm accumulates less experience. As a result, the throughput is lower than that of the FL-HO algorithm at v \(<\) 1.5 m/s. As the speed increases, the algorithm is triggered more frequently, and it can learn faster to select a more suitable network (e.g., Wi-Fi) for fast-moving users. When the user speed is 4 m/s, the throughput of the algorithm in this paper is about 42% higher than that of the traditional STD-LTE scheme and about 27% higher than that of the FL-HO scheme. Figure 8 shows the variation of the system throughput with the number of room users in the case of humans' normal indoor walking speed of 2 m/s. With the increase in the number of users, the throughput of STD-LTE and fuzzy logic methods gradually decreases, while the throughput of this paper's algorithm remains relatively stable. When the number of users is 22-26 people, the throughput slightly decreases because of the increased competition for the system resources. However, it is still higher than the other two comparison algorithms. When the number of users reaches 26, the throughput is 462 Mbps, about 33% higher than the STD-LTE scheme, and about 15% higher than the FL-HO scheme. Figure 8: Variation of the average throughput for different numbers of people. Figure 7: Variation of the average throughput for different users’ speed. Figure 9 demonstrates the effect of the different optical path-blocking incidence \(\lambda\) on the system throughput, where the number of users in the room is set to 15 and the speed is 2 m/s. The optical path blocking rate indicates the average number of times that the optical path is blocked per unit of time. Modeling the optical path blocking as a Poisson process, uniformly distributed between 0 and 1, ensures that the average number of blocking events occurring per unit of time is constant and each blocking event occurs independently. When \(\lambda=2\), the frequency of blockings are low, and handovers are primarily driven by user mobility. The throughput of our proposed scheme is 28% higher than that of STD-LTE scheme. The FL-HO scheme which also considers user mobility in its decision-making process has a performance close to that of the proposed scheme when \(\lambda\) is low. As \(\lambda\) increases, the throughput of all schemes decreases. However, the decreasing trend of our proposed scheme is slower. When \(\lambda\) increases from 2 times/minute to 10 times/minute, the throughput achieved by the proposed scheme drops from 461 Mbps to 408 Mbps (only 11%). The throughput of STD-LTE decreases by 35%, and the throughput of FL-HO decreases by 22%.