AI-based Models for Resource Allocation and Resource Demand Forecasting Systems in Aviation: A Survey and Analytical Study

Hejji, Dina; Talib, Manar Abu; Nassif, Ali Bou; Nasir, Qassim; Bouridane, Ahmed

doi:10.1109/iotais53735.2021.9628555

Cited by 2 publications

(2 citation statements)

References 44 publications

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“…In addition, authors in [26] formulate a reinforcement learning algorithm for resource allocation in device-to-device (D2D) communications, whereas authors in [27] propose a recurrent neural network (RNN) model based on meta-learning to predict the millimeter wave (mmWave) link blockages. Mohammadi et al [28] presented a multiagent deep reinforcement learning (DRL) solution for resource allocation, authors in [29] proposed a clustering-based solution to perform resource scheduling depending on each cluster priority and demands, and the work in [30] presented a survey of recent artificial intelligent (AI)-based frameworks for resource allocation in diverse use cases. Table II summarizes the existing reviewed literature.…”

Section: State Of the Artmentioning

confidence: 99%

“…. by the random variable GF-RA S. M. Hasan et al [21] NOMA FU grant Z. Zhou et al [22] Hybrid resource allocation FU grant S. Ali et al [23] Multi-armed bandit E. Eldeeb et al [24] SVM and LSTM O. Habachi et al [25] Federated learning Deep learning I. AlQerm et al [26] Reinforcement learning A. E. Kalør et al [27] Meta-learning and RNN F. Mohammadi et al [28] Multi-agent DRL X. Liu et al [29] Clustering D. Hejji et al [30] AI survey…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

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Traffic Prediction and Fast Uplink for Hidden Markov IoT Models

Eldeeb

Shehab

Kalør

et al. 2022

IEEE Internet Things J.

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In this work, we present a novel traffic prediction and fast uplink framework for IoT networks controlled by binary Markovian events. First, we apply the forward algorithm with hidden Markov models (HMM) in order to schedule the available resources to the devices with maximum likelihood activation probabilities via fast uplink grant. In addition, we evaluate the regret metric as the number of wasted transmission slots to evaluate the performance of the prediction. Next, we formulate a fairness optimization problem to minimize the age of information while keeping the regret as minimum as possible. Finally, we propose an iterative algorithm to estimate the model hyperparameters (activation probabilities) in a real-time application and apply an online-learning version of the proposed traffic prediction scheme. Simulation results show that the proposed algorithms outperform baseline models such as time division multiple access (TDMA) and grant-free (GF) random-access in terms of regret, the efficiency of system usage, and age of information.

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