Predicting Bursty Traffic in M2M Networks

Paper Title:

RACH Traffic Prediction in Massive Machine Type Communications

Published on:

8 May 2024

Primary Category:

Systems and Control

Paper Authors:

Hossein Mehri,

Hao Chen,

Hani Mehrpouyan

Bullets

Key Details

•

Proposes ML framework combining LSTM and DenseNets to predict bursty traffic

•

Develops efficient online prediction algorithm that leverages real-time data

•

Achieves 52% higher accuracy than traditional methods

•

Identifies congestion events before they occur

•

Well-suited for time-critical, resource-constrained applications

Explore the topics in this paper

congestion prediction

efficiency

machine learning

neural networks

traffic forecasting

AI generated summary

Predicting Bursty Traffic in M2M Networks

This paper presents a machine learning framework to forecast bursty traffic patterns in massive machine-type communication networks. It uses long short-term memory and dense neural networks to accurately predict traffic and identify congestion events by assimilating real-time data. A new online prediction algorithm updates model states efficiently. Evaluations demonstrate 52% higher accuracy without additional overhead.