## 5 Conclusions  
To improve the efficiency and stability of indoor VLC/Wi-Fi heterogeneous network handover, this paper proposes a network handover algorithm based on mobility trajectory prediction, suitable for VHO and HHO in heterogeneous networks. The algorithm simplifies the indoor spatial model, and by analyzing the user's historical movement data and combining it with the improved HMM algorithm, the user's position at the next moment can be accurately predicted, enabling more accurate handover decisions. Specifically, when the user speed is 4 m/s, the proposed algorithm improves throughput performance by approximately 42% compared to the traditional handover algorithm. As the number of users increases, the proposed algorithm better maintains high fairness. When the number of connected users reaches 26, the throughput is improved by about 33% over the traditional algorithm. In terms of handover rate, as the user speed increases, the algorithm proposed in this paper can reduce the HHO rate by 42% and the VHO rate by 32%. Although the proposed algorithm performs well in most scenarios, the trajectory prediction module relies on the completeness and consistency of historical data. The prediction accuracy may decline when sudden changes occur in the user's movement pattern or when the data are insufficient. An online learning framework can be introduced to enable the model to dynamically update parameters and adapt to changes in user behavior in real-time. The network handover algorithm proposed in this paper accounts for two key factors: user mobility and network signal quality. However, when additional environmental factors are introduced, such as network load and user preferences, the state space becomes more complex. As a result, Q-learning may experience slower convergence and higher computational complexity. Future research can further explore advanced multi-objective optimization methods to better balance these parameters. Moreover, advanced reinforcement learning algorithms can be implemented to handle high-dimensional state spaces, ensuring more stable communication services for users. Conceptualization, C.K. and M.W. ; Data curation, C.K. and M.W. ; Formal analysis, C.K. ; Funding acquisition, C.K. and H.Q. ; Investigation, C.K. ; Methodology, C.K. and M.W. ; Project administration, M.W. ; Resources, M.W. ; Software, M.W. ; Supervision, C.K. ; Validation, C.K., M.W., H.Q. and X.K. ; Writing--original draft, M.W. ; Writing--review and editing, C.K., M.W., H.Q. and X.K. All authors have read and agreed to the published version of the manuscript. The National Natural Science Foundation of China [No. 61377080]; Special Research Program of Shaanxi Provincial Department of Education [22]K0530]; Xi'an Science and Technology Plan Project [No. 23 KGDW0018-2023]; Natural Science Basic Research Program of Shaanxi [Program  
Figure 13: Convergence with different number of users. No. 2024 JC-YBMS-557, 2024 JC-YBMS-562]; Shaanxi Fundamental Science Research Project for Mathematics and Physics [Grant No. 23 JSQ024]; Key Industrial Innovation Projects in Shaanxi Province [No. 2017 ZDCXL-GY-06-01]; the Xi'an Science and Technology Plan [22 GXFW0115]; the Scientific Research Team of Xi'an University [D202309]. This study did not require ethical approval. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest. * [PERSON] and [PERSON] (2024) [PERSON]; [PERSON] Heterogeneous Lifi-WiFi with multipath transmission protocol for effective access point selection and load balancing. _Wirel. Netw._**2024**, _30_, 2423-2437. [CrossRef]
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