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  1. Home
  2. Privacy-preserving Handover Optimization Using Federated Learning And Lstm Networks.
  1. Home
  2. Privacy-preserving Handover Optimization Using Federated Learning And Lstm Networks.

Related Experiment Video

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Privacy-Preserving Handover Optimization Using Federated Learning and LSTM Networks.

Wei-Che Chien1, Yu Huang1, Bo-Yu Chang1

  • 1Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien City 974301, Taiwan.

Sensors (Basel, Switzerland)
|October 26, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a new dynamic handover algorithm using Federated Learning and Long Short-Term Memory networks. It improves wireless network performance by accurately predicting signal strength and reducing unnecessary handovers.

Keywords:
LSTMfederated learningpredictionreference signal received power

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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.