  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.
