Authors: Hossein Mehri, Hao Chen, Hani Mehrpouyan

Published on: May 08, 2024

Impact Score: 7.4

Arxiv code: Arxiv:2405.05239

Summary

• What is new: Introduction of a novel Fast LiveStream Prediction (FLSP) algorithm and its application to 5G network traffic prediction.
• Why this is important: The dynamic and heterogeneous nature of 5G network traffic requires sophisticated prediction models for efficient resource allocation and management.
• What the research proposes: Two live prediction algorithms, including the new FLSP, were tested on machine learning models to forecast 5G network traffic in real-time, focusing on synchronous and asynchronous data reporting.
• Results: The FLSP algorithm significantly outperformed traditional online prediction algorithms, especially in asynchronous settings, by reducing required bandwidth by half, improving prediction accuracy, and lowering processing demands.

Technical Details

Technological frameworks used: Live prediction algorithms applied to machine learning models

Models used: Fast LiveStream Prediction (FLSP) algorithm and conventional online prediction algorithms.

Data used: Synchronous and asynchronous cellular network traffic data.

Potential Impact

Telecommunications companies, 5G network service providers, and mobile network operators could leverage these findings to optimize network performance and resource management, potentially affecting network equipment manufacturers and software solution providers.

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