Privacy-Preserving Handover Optimization Using Federated Learning and LSTM Networks.

Area of Science:

• Wireless Communication Systems
• Machine Learning Applications
• Network Performance Optimization

Background:

• Traditional handover algorithms like 3GPP Event A3 face challenges with fluctuating signal strengths and user mobility.
• These limitations result in frequent, suboptimal handovers and inefficient resource utilization in wireless networks.

Purpose of the Study:

• To develop an advanced, data-driven handover mechanism for wireless communication systems.
• To enhance network performance by improving handover decision accuracy and efficiency.
• To ensure data privacy through a Federated Learning approach.

Main Methods:

• Combining Federated Learning (FL) for privacy-preserving data analysis and Long Short-Term Memory (LSTM) networks for temporal signal prediction.
• Developing a dynamic handover algorithm that adapts thresholds based on predicted Reference Signal Received Power (RSRP) and historical performance.
• Utilizing real-world data for extensive experimental validation.

Main Results:

• The proposed dynamic handover algorithm significantly outperforms the traditional 3GPP Event A3 algorithm.
• Achieved higher prediction accuracy for Reference Signal Received Power (RSRP).
• Demonstrated a reduction in unnecessary handovers and improved overall network performance.

Conclusions:

• The novel FL-LSTM approach offers a more efficient and reliable handover mechanism for future wireless networks.
• This data-driven, privacy-preserving method enhances connectivity and network resource utilization.
• The dynamic algorithm provides adaptive performance crucial for evolving wireless environments.