Clustered temporal memory networks: A hybrid approach for signal strength prediction

Claude Mukatshung Nawej (), Pius Adewale Owolawi () and Tom Walingo ()

Edelweiss Applied Science and Technology, 2024, vol. 8, issue 6, 6625-6643

Abstract: The rapid expansion of 5G networks has revolutionized mobile communication by offering unprecedented speed, low-latency connections, and the ability to support vast numbers of connected devices. However, these advancements bring new challenges in maintaining consistent and reliable signal strength, critical for ensuring optimal Quality of Service (QoS). Traditional models, such as ARIMA, Random Forest (RF), and K-means clustering, struggle to capture the complex, nonlinear, and dynamic behaviour of 5G networks, leading to suboptimal prediction accuracy. In this study, we propose a novel hybrid model, Clustered Temporal Memory Networks (CTMN), which integrates DBSCAN clustering with Long Short-Term Memory (LSTM) networks to improve signal strength prediction in mobile networks. The CTMN model combines DBSCAN's ability to handle spatial variability and outliers in 5G data, combined with LSTM's capacity for modelling long-term dependencies and nonlinear time-series patterns. Our empirical analysis demonstrates that CTMN outperforms traditional methods, achieving up to a 20.82% improvement in prediction accuracy across key performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). These findings indicate that CTMN provides a scalable, robust solution for enhancing signal strength prediction and optimizing network performance in next-generation mobile networks.

Keywords: Clustering; Hybrid techniques; Mobile networks; Prediction model; Time-series analysis. (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://learning-gate.com/index.php/2576-8484/article/view/3415/1287 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ajp:edwast:v:8:y:2024:i:6:p:6625-6643:id:3415

Access Statistics for this article

More articles in Edelweiss Applied Science and Technology from Learning Gate
Bibliographic data for series maintained by Melissa Fernandes ().