The emergence of 5G communication systems will not replace existing radio access networks but will gradually merge to form ultra-dense heterogeneous networks. In heterogeneous networks, the design of efficient vertical handoff (VHO) algorithms for 5G infrastructures is necessary to improve quality of service (QoS) and system resource utilization. In this paper, an optimized algorithm based on a multi-objective optimization model is proposed to solve the lack of a comprehensive consideration of user and network impacts during the handoff process in existing VHO algorithms. The Markov chain model of each base station (BS) is built to calculate a more accurate value of the network state that reflects the network performance. Then, a multi-objective optimization model is derived to maximize the value of the network state and the user data receiving rate. The multi-objective genetic algorithm NSGA-II is finally employed to turn the model into a final VHO strategy. The results of the simulation for the throughput and blocking rate of networks demonstrate that our algorithm significantly increases the system throughput and reduces the blocking rate compared to the existing VHO strategies.

5G通信系统的出现将不会取代现有的无线电接入网络，而是将逐渐合并以形成超密集的异构网络。在异构网络中，为5G基础架构设计有效的垂直切换（VHO）算法对于提高服务质量（QoS）和系统资源利用率至关重要。本文提出了一种基于多目标优化模型的优化算法，以解决现有VHO算法在切换过程中对用户和网络影响缺乏综合考虑的问题。建立每个基站（BS）的马尔可夫链模型来计算反映网络性能的更准确的网络状态值。然后，推导出一个多目标优化模型以最大化网络状态值和用户数据接收速率。最后，采用多目标遗传算法NSGA-II将模型转变为最终的VHO策略。网络吞吐量和阻塞率的仿真结果表明，与现有的VHO策略相比，我们的算法显着提高了系统吞吐量，并降低了阻塞率。