Analysis of Uplink Scheduling for Haptic Communications

Mahlouji, Maliheh; Mahmoodi, Toktam

doi:10.1109/glocomw.2018.8644406

Cited by 4 publications

(1 citation statement)

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“…Such mechanisms can be deployed at the coordinator side to keep the MTDs simple and energy-efficient. Moreover, the coordinators can control the medium access, avoid collisions, and consequently reduce the energy consumed by the MTDs when they are not scheduled and would be performing idle listening [45], [46]. For the sake of contextualization, consider the following potential scenarios: i) target tracking, and ii) visitors of a historic arena.…”

Section: System Modelmentioning

confidence: 99%

Energy-Efficient Wake-Up Signalling for Machine-Type Devices Based on Traffic-Aware Long Short-Term Memory Prediction

Ruíz-Guirola

Rodríguez-López

Montejo‐Sánchez³

et al. 2022

IEEE Internet Things J.

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educing energy consumption is a pressing issueeducing energy consumption is a pressing issueR in low-power machine-type communication (MTC) networks. In this regard, the Wake-up Signal (WuS) technology, which aims to minimize the energy consumed by the radio interface of the machine-type devices (MTDs), stands as a promising solution. However, stateof-the-art WuS mechanisms use static operational parameters, so they cannot efficiently adapt to the system dynamics. To overcome this, we design a simple but efficient neural network to predict MTC traffic patterns and configure WuS accordingly. Our proposed forecasting WuS (FWuS) leverages an accurate long-short term memory (LSTM)-based traffic prediction that allows extending the sleep time of MTDs by avoiding frequent page monitoring occasions in idle state. Simulation results show the effectiveness of our approach. The traffic prediction errors are shown to be below 4%, being false alarm and miss-detection probabilities respectively below 8.8% and 1.3%. In terms of energy consumption reduction, FWuS can outperform the best benchmark mechanism in up to 32%. Finally, we certify the ability of FWuS to dynamically adapt to traffic density changes, promoting low-power MTC scalability.

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Section: System Modelmentioning

confidence: 99%