Abstract:
This project introduces a binary Markovian event-controlled IoT network traffic prediction and fast uplink (FU) framework. The forward algorithm with hidden Markov models (HMMs) schedules resources to devices with maximum likelihood activation probabilities via the FU grant.
To assess prediction performance, we calculate the regret metric as the number of wasted transmission slots. Next, we formulate a fairness optimization problem to minimize the Age of Information (AoI) while minimizing regret.
Finally, we propose a real-time iterative algorithm to estimate model hyperparameters (activation probabilities) and apply an online-learning traffic prediction scheme. Simulation results show that the proposed algorithms outperform baseline models like TDMA and grant-free (GF) random-access in regret, system efficiency, and AoI.
Keywords : Hidden Markov Models, Internet Of Things, Sensors, Uplink, Resource Management, Time Division Multiple Access, Prediction Algorithms, Hidden Markov Models, Information Theory, Internet Of Things, Iterative Methods, Maximum Likelihood Estimation, Probability, Telecommunication Traffic, Time Division Multiple Access
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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