In this work, we present a novel traffic prediction and fast uplink (FU) framework for IoT networks controlled by binary Markovian events. First, we apply the forward algorithm with hidden Markov models (HMMs) in order to schedule the available resources to the devices with maximum likelihood activation probabilities via the FU 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 (AoI) 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 AoI.

中文翻译：

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隐马尔可夫物联网模型的流量预测和快速上行链路

在这项工作中，我们为由二进制马尔可夫事件控制的物联网网络提出了一种新颖的流量预测和快速上行链路 (FU) 框架。首先，我们应用带有隐马尔可夫模型 (HMM) 的前向算法，以便通过 FU 授权将可用资源调度到具有最大似然激活概率的设备。此外，我们将后悔度量评估为浪费的传输时隙的数量，以评估预测的性能。接下来，我们制定了一个公平优化问题，以最小化信息时代（AoI），同时尽可能地减少遗憾。最后，我们提出了一种迭代算法来估计实时应用中的模型超参数（激活概率），并应用所提出的交通预测方案的在线学习版本。