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Traffic Prediction Based Fast Uplink Grant for Massive IoT

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Document pages: 6 pages

Abstract: This paper presents a novel framework for traffic prediction of IoT devicesactivated by binary Markovian events. First, we consider a massive set of IoTdevices whose activation events are modeled by an On-Off Markov process withknown transition probabilities. Next, we exploit the temporal correlation ofthe traffic events and apply the forward algorithm in the context of hiddenMarkov models (HMM) in order to predict the activation likelihood of each IoTdevice. Finally, we apply the fast uplink grant scheme in order to allocateresources to the IoT devices that have the maximal likelihood for transmission.In order to evaluate the performance of the proposed scheme, we define theregret metric as the number of missed resource allocation opportunities. Theproposed fast uplink scheme based on traffic prediction outperforms bothconventional random access and time division duplex in terms of regret andefficiency of system usage, while it maintains its superiority over randomaccess in terms of average age of information for massive deployments.

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