A novel vertical handover algorithm based on multi-attribute and neural network for heterogeneous integrated network is proposed in this paper. The whole frame of the algorithm is constructed by setting the network environment in which we use the network resources by switching between UMTS, GPRS, WLAN, 4G, and 5G. Each network build their own three-layer BP (Back Propagation, BP) neural network model and then the maximum transmission rate, minimum delay, SINR (signal to interference and noise ratio, SINR), bit error rate, user moving speed, and packet loss rate which can affect the overall performance of the wireless network are employed as reference objects to participate in the setting of BP neural network input layer neurons and the training and learning process of subsequent neural network data. Finally, the network download rate is adopted as prediction target to evaluate performance on the five wireless networks and then the vertical handover algorithm will select the right wireless network to perform vertical handover decision. The simulation results on MATLAB platform show that the vertical handover algorithm designed in this paper has a handover success rate up to 90% and realizes efficient handover and seamless connectivity between multi-heterogeneous networks.

提出了一种基于多属性和神经网络的异构集成网络垂直切换算法。该算法的整个框架是通过在UMTS，GPRS，WLAN，4G和5G之间切换来设置我们使用网络资源的网络环境而构建的。每个网络都建立自己的三层BP（反向传播，BP）神经网络模型，然后建立最大传输速率，最小延迟，SINR（信噪比，SINR），误码率，用户移动速度和数据包会影响无线网络整体性能的丢失率被用作参考对象，以参与BP神经网络输入层神经元的设置以及后续神经网络数据的训练和学习过程。最后，以网络下载速率作为预测目标，以评估五个无线网络的性能，然后垂直切换算法将选择合适的无线网络来执行垂直切换决策。在MATLAB平台上的仿真结果表明，本文设计的垂直切换算法具有高达90％的切换成功率，实现了高效的切换和多异构网络之间的无缝连接。