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

arXiv: https://arxiv.org/abs/2206.06058 · HTML: https://mlsanity.com/paper/2206.06058/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 69 references that are in the map.
- First posted on arXiv 13 Jun 2022.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [A Learning-Based Fast Uplink Grant for Massive IoT via Support Vector Machines and Long Short-Term Memory](https://mlsanity.com/paper/2108.10070/) (2021)
- [Integrated Access Backhauled Networks](https://mlsanity.com/paper/1906.09298/) (2019)
- [IoT Connectivity Technologies and Applications: A Survey](https://mlsanity.com/paper/2002.12646/) (2020)
- [Traffic Prediction Based Fast Uplink Grant for Massive IoT](https://mlsanity.com/paper/2008.02207/) (2020)
- [A Traffic Model for Machine-Type Communications Using Spatial Point Processes](https://mlsanity.com/paper/1709.00867/) (2017)
- [CSI-free vs CSI-based multi-antenna WET for massive low-power Internet of Things](https://mlsanity.com/paper/2006.00030/) (2020)
- [Analysis of Uplink Scheduling for Haptic Communications](https://mlsanity.com/paper/1809.09837/) (2018)
- [Ultra-Low-Power IoT Communications: A novel address decoding approach for wake-up receivers](https://mlsanity.com/paper/2111.07607/) (2021)

## Built on by (most cited first, 2 of 2)

- [Intelligent Duty Cycling Management and Wake-up for Energy Harvesting IoT Networks with Correlated Activity](https://mlsanity.com/paper/2405.06372/) (2024)
- [Modeling IoT Traffic Patterns: Insights from a Statistical Analysis of an MTC Dataset](https://mlsanity.com/paper/2409.01932/) (2024)

## Abstract (arXiv)

Reducing energy consumption is a pressing issue 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, state-of-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