# A Learning-Based Fast Uplink Grant for Massive IoT via Support Vector Machines and Long Short-Term Memory

arXiv: https://arxiv.org/abs/2108.10070 · HTML: https://mlsanity.com/paper/2108.10070/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 2 Aug 2021.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Visualizing and Understanding Recurrent Networks](https://mlsanity.com/paper/1506.02078/) (2015)
- [Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark](https://mlsanity.com/paper/1510.03336/) (2015)
- [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)
- [Wireless Access in Ultra-Reliable Low-Latency Communication (URLLC)](https://mlsanity.com/paper/1810.06938/) (2018)
- [Traffic Prediction Based Fast Uplink Grant for Massive IoT](https://mlsanity.com/paper/2008.02207/) (2020)
- [LTE/LTE-A Random Access for Massive Machine-Type Communications in Smart Cities](https://mlsanity.com/paper/1608.03042/) (2016)
- [Six Key Enablers for Machine Type Communication in 6G](https://mlsanity.com/paper/1903.05406/) (2019)

## Built on by (most cited first, 7 of 7)

- [A Multi-Task Oriented Semantic Communication Framework for Autonomous Vehicles](https://mlsanity.com/paper/2403.12997/) (2024)
- [Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Models](https://mlsanity.com/paper/2401.13827/) (2024)
- [Energy-Efficient Wake-Up Signalling for Machine-Type Devices Based on Traffic-Aware Long-Short Term Memory Prediction](https://mlsanity.com/paper/2206.06058/) (2022)
- [LoRaWAN-enabled Smart Campus: The Dataset and a People Counter Use Case](https://mlsanity.com/paper/2304.13366/) (2023)
- [Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification](https://mlsanity.com/paper/2409.12978/) (2024)
- [RACH Traffic Prediction in Massive Machine Type Communications](https://mlsanity.com/paper/2405.05235/) (2024)
- [Modeling IoT Traffic Patterns: Insights from a Statistical Analysis of an MTC Dataset](https://mlsanity.com/paper/2409.01932/) (2024)

## Abstract (arXiv)

The current random access (RA) allocation techniques suffer from congestion and high signaling overhead while serving massive machine type communication (mMTC) applications. To this end, 3GPP introduced the need to use fast uplink grant (FUG) allocation in order to reduce latency and increase reliability for smart internet-of-things (IoT) applications with strict QoS constraints. We propose a novel FUG allocation based on support vector machine (SVM), First, MTC devices are prioritized using SVM classifier. Second, LSTM architecture is used for traffic prediction and correction techniques to overcome prediction errors. Both results are used to achieve an efficient resource scheduler in terms of the average latency and total throughput. A Coupled Markov Modulated Poisson Process (CMMPP) traffic model with mixed alarm and regular traffic is applied to compare the proposed FUG allocation to other existing allocation techniques. In addition, an extended traffic model based CMMPP is used to evaluate the proposed algorithm in a more dense network. We test the proposed scheme using real-time measurement data collected from the Numenta Anomaly Benchmark (NAB) database. Our simulation results show the proposed model outperforms the existing RA allocation schemes by achieving the highest throughput and the lowest access delay of the order of 1 ms by achieving prediction accuracy of 98 % when serving the target massive and critical MTC applications with a limited number of resources.