A Learning-Based Fast Uplink Grant for Massive IoT via Support Vector Machines and Long Short-Term Memory
Eslam Eldeeb, Mohammad Shehab, Hirley Alves
Abstract
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, third-generation partnership project 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 Quality-of-Service constraints. We propose a novel FUG allocation based on support vector machine (SVM). First, machine-type communication (MTC) devices are prioritized using an SVM classifier. Second, a long short-term memory 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.