Enhancing mobility management in 5G networks using deep residual LSTM model

Title

Enhancing mobility management in 5G networks using deep residual LSTM model

Creator

Baz A.; Logeshwaran J.; Natarajan Y.; Patel S.K.

Description

Mobility management is an essential component of 5G networks to provide mobile users with seamless connectivity and efficient cell transition. However, increasing user mobility, device density, and the diversity of service requirements all pose significant challenges to achieving optimal mobility management. This article describes a novel method for improving mobility management in 5G networks that employs a deep residual Long Short-Term Memory model. Deep learning and LSTM, a type of recurrent neural network, are used in the proposed model to identify temporal dependencies and patterns in user mobility data. The model learns to predict future user locations and mobility patterns by training on historical mobility data, allowing for proactive resource allocation and handover decisions. We incorporate residual connections into the LSTM architecture, inspired by the residual learning framework, to address the inability of traditional LSTM models to capture complex temporal dynamics. This allows the model to effectively incorporate long-term dependencies and improves prediction accuracy. Furthermore, we incorporate the mLSTM model into the mobility management framework of 5G networks. The model continuously obtains real-time user location updates and predicts future user positions, allowing for proactive handover decisions. The network can optimize resource allocation, reduce handover latency, and improve user experience by leveraging anticipated mobility patterns. We test the proposed method by simulating it extensively with real-world mobility traces. The results show that the mLSTM model accurately predicts user mobility and outperforms conventional methods in transition performance. The model is not affected by changing network conditions, user mobility patterns, or service specifications. 2024 Elsevier B.V.

Source

Applied Soft Computing, Vol-165

Date

2024-01-01

Publisher

Elsevier Ltd

Subject

5G networks; Handover optimization; LSTM; Mobility management; Resource Allocation

Coverage

Baz A., Department of Computer and Network Engineering, College of Computing, Umm Al-Qura University, Makkah, Saudi Arabia; Logeshwaran J., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Natarajan Y., Department of Computer Science and Engineering, Sri Shakthi Institute of Engineering and Technology, Tamil Nadu, Coimbatore, 641062, India; Patel S.K., Department of Computer Engineering, Marwadi University, Gujarat, Rajkot, 360003, India

Rights

Restricted Access

Relation

ISSN: 15684946

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1016/j.asoc.2024.112103

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202155517&doi=10.1016%2fj.asoc.2024.112103&partnerID=40&md5=8e04a5aa78f775d9789c9dffbe538692