# RACH Traffic Prediction in Massive Machine Type Communications

arXiv: https://arxiv.org/abs/2405.05235 · HTML: https://mlsanity.com/paper/2405.05235/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 61 references that are in the map.
- First posted on arXiv 8 May 2024.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Evaluation of deep learning models for multi-step ahead time series prediction](https://mlsanity.com/paper/2103.14250/) (2021)
- [Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions](https://mlsanity.com/paper/1808.02924/) (2018)
- [ResNet or DenseNet? Introducing Dense Shortcuts to ResNet](https://mlsanity.com/paper/2010.12496/) (2020)
- [A Learning-Based Fast Uplink Grant for Massive IoT via Support Vector Machines and Long Short-Term Memory](https://mlsanity.com/paper/2108.10070/) (2021)
- [Traffic Prediction Based Fast Uplink Grant for Massive IoT](https://mlsanity.com/paper/2008.02207/) (2020)
- [Traffic Prediction and Random Access Control Optimization: Learning and Non-learning based Approaches](https://mlsanity.com/paper/2002.07759/) (2020)
- [LTE/LTE-A Random Access for Massive Machine-Type Communications in Smart Cities](https://mlsanity.com/paper/1608.03042/) (2016)
- [Towards a Data-driven IoT Software Architecture for Smart City Utilities](https://mlsanity.com/paper/1803.02500/) (2018)
- [Event-Driven Source Traffic Prediction in Machine-Type Communications Using LSTM Networks](https://mlsanity.com/paper/2101.04365/) (2021)

## Abstract (arXiv)

Traffic pattern prediction has emerged as a promising approach for efficiently managing and mitigating the impacts of event-driven bursty traffic in massive machine-type communication (mMTC) networks. However, achieving accurate predictions of bursty traffic remains a non-trivial task due to the inherent randomness of events, and these challenges intensify within live network environments. Consequently, there is a compelling imperative to design a lightweight and agile framework capable of assimilating continuously collected data from the network and accurately forecasting bursty traffic in mMTC networks. This paper addresses these challenges by presenting a machine learning-based framework tailored for forecasting bursty traffic in multi-channel slotted ALOHA networks. The proposed machine learning network comprises long-term short-term memory (LSTM) and a DenseNet with feed-forward neural network (FFNN) layers, where the residual connections enhance the training ability of the machine learning network in capturing complicated patterns. Furthermore, we develop a new low-complexity online prediction algorithm that updates the states of the LSTM network by leveraging frequently collected data from the mMTC network. Simulation results and complexity analysis demonstrate the superiority of our proposed algorithm in terms of both accuracy and complexity, making it well-suited for time-critical live scenarios. We evaluate the performance of the proposed framework in a network with a single base station and thousands of devices organized into groups with distinct traffic-generating characteristics. Comprehensive evaluations and simulations indicate that our proposed machine learning approach achieves a remarkable 52% higher accuracy in long-term predictions compared to traditional methods, without imposing additional processing load on the system.