# Privacy-Preserving Handover Optimization Using Federated Learning and LSTM Networks
###### Abstract  
The rapid evolution of wireless communication systems necessitates advanced handover mechanisms for seamless connectivity and optimal network performance. Traditional algorithms, like 3 GPP Event A3, often struggle with fluctuating signal strengths and dynamic user mobility, leading to frequent handovers and suboptimal resource utilization. This study proposes a novel approach combining Federated Learning (FL) and Long Short-Term Memory (LSTM) networks to predict Reference Signal Received Power (RSRP) and the strongest nearby Reference Signal Received Power (RSRP) signals. Our method leverages FL to ensure data privacy and LSTM to capture temporal dependencies in signal data, enhancing prediction accuracy. We develop a dynamic handover algorithm that adapts to real-time conditions, adjusting thresholds based on predicted signal strengths and historical performance. Extensive experiments with real-world data show our dynamic algorithm significantly outperforms the 3 GPP Event A3 algorithm, achieving higher prediction accuracy, reducing unnecessary handovers, and improving overall network performance. In conclusion, this study introduces a data-driven, privacy-preserving approach that leverages advanced machine learning techniques, providing a more efficient and reliable handover mechanism for future wireless networks.  
federated learning; LSTM; reference signal received power; prediction +
Footnote †: journal:  
0000-0001-4000-0000-0000-0000-0000-0000-0000-0000-0000-0  
The key parameters for handover control parameters (HCPs) are the time-to-trigger (TTT) and the handover margin (HOM) [7] Figure 1. Proper configuration of these parameters is crucial because it helps mitigate conflicting objectives that may arise during HCP optimization. For instance, setting a high TTT can increase the risk of radio link failure (RLF), while a low TTT setting can elevate the probability of handover ping-pong (HOPP). To address these issues, dynamically adjusting HOM and TTT values is essential. By continuously tuning these parameters based on real-time network conditions, it is possible to balance the trade-offs between RLF and HOPP, thereby ensuring a more stable and efficient network performance [8; 9]. Dynamic adjustments of TTT and HOM can significantly contribute to reducing related issues, providing a more adaptable and resilient network that can better handle varying conditions and demands.  
LSTM networks can efficiently learn Reference Signal Received Power (RSRP) and the strongest nearby Reference Signal Received Power (NR-RSRP). However, traditional centralized machine learning often overlooks potential privacy issues. To address this, we combined Federated Learning (FL) with our approach, allowing clients to transmit trained model weights during the training process, thereby preventing the leakage of sensitive data. We utilized the FedAvg algorithm for model aggregation, which helps the model avoid local minima, resulting in performance that surpasses centralized LSTM models. Additionally, we conducted experiments on both heterogeneous and homogeneous federated learning to assess the performance differences under varying device computing capabilities and network conditions. Therefore, this study aims to develop a novel approach that combines Federated Learning (FL) and Long Short-Term Memory (LSTM) networks to predict Reference Signal Received Power (RSRP) and the strongest nearby Reference Signal Received Power (RSRP) signals. By leveraging FL, we ensure data privacy through decentralized model training, while LSTM networks capture temporal dependencies and complex patterns in the signal data, improving prediction accuracy. Additionally, we propose a new dynamic handover algorithm that adjusts to real-time network conditions and user mobility patterns, enhancing handover decision-making and reducing unnecessary handovers.  
Figure 1: Handover Decision Process with TTT and HOM.