Enhancing handover management in 5G networks with encoder-decoder LSTM for multistep forecasting

Abstract

The continuous evolution of wireless communication networks, fueled by advancements in 5G and the envisioned potential of 6G technologies, has introduced significant challenges in mobility management and handover (HO) optimization. The frequent HOs due to network densification, particularly at high frequencies like millimeter waves (mmWave) and terahertz (THz) bands, can lead to increased latency, and potential service disruptions. To address these issues, artificial intelligence (AI) driven approaches are emerging as promising alternatives. This paper explores the use of deep learning techniques for predictive HO management. An encoder-decoder long short-term memory (ED-LSTM) model is proposed to generate multistep predictions of future reference signal received power (RSRP) values. The model was trained and evaluated on two distinct real-world drive-test datasets. The results demonstrate that the proposed ED-LSTM model achieves lower prediction error, with a mean absolute error (MAE) of 2.07 for dataset 1 and 2.33 for dataset 2, and a mean absolute percentage error (MAPE) of 2.80% for dataset 1 and 2.96% for dataset 2. Overall, the ED-LSTM outperforms the bidirectional LSTM (BiLSTM) and standard LSTM (S-LSTM) model, achieving improvements of 33–38% on dataset 1 and 48-50% on dataset 2 in terms of MAE and MAPE, respectively.