Accurate Seamless Vertical Handover Prediction Using Peephole LSTM Based on Light-GBM Algorithm in Heterogeneous Cellular Networks

Present and future mobile networks combine wireless radio access technologies from multiple cellular network generations, all of which coexist. Seamless Vertical Handover (VH) decision-making is still a challenging issue in heterogeneous cellular networks due to the dynamic conditions of networks, different demands on QoS, and the latency of the handover process. Maintaining a very high-accuracy VH decision requires considering several network parameters. There is a trade-off between the gain of the VH accuracy and the corresponding latency in the computational complexity of the decision-making methods. This paper proposes a lightweight VH prediction DL strategy for 3G, 4G, and 5G networks based on the Light-Gradient Boosting Machine (LGBM) feature selection and Peephole Long Short-Term Memory (PLSTM) prediction model. For dense networks with large datasets and high-dimensional data, the combination of PLSTM and the fast feature selection LGBM, can reduce the computing complexity while preserving prediction accuracy and excellent performance levels. The proposed methods are evaluated using three case study scenarios using different feature selection thresholds. The performance evaluation is achieved by training and testing the proposed model, which shows an improvement using the proposed LGBM and PLSTM in terms of reducing the number of features by 64.28% and enhancing the VH accuracy prediction by 43.81% in Root Mean Squared Error (RMSE), and reducing the VH decision time of up to 51%. Furthermore, a network simulation using the proposed VH prediction algorithm shows an enhancement in overall network performance, with the number of successful VHs being 87%. Consequently, the data throughput is significantly enhanced.