Rectenna Designs for Energy Harvesting IoT Devices: Overview of Current Trends and Future Directions

Umeonwuka, Obumneme Obiajulu; Adejumobi, Babatunde S.; Shongwe, Thokozani

doi:10.1109/icecet52533.2021.9698604

Cited by 4 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These harvesting tools are referred to as Rectenna, which stands for RECtifying an-TENNA. As illustrated in Figure 3 [40], [41], a rectenna circuit typically consists of a receiving antenna coupled to an RF bandpass filter, a rectifier, a low-pass filter, and ultimately a load (or energy storage device).…”

Section: A Radio Frequency Energy Harvestingmentioning

confidence: 99%

Deep Learning-Assisted Energy Prediction Modeling for Energy Harvesting in Wireless Cognitive Radio Devices

Umeonwuka,

Adejumobi,

Shongwe

2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

Cognitive radio is a technology that allows Secondary Users (SUs) to access vacant spectrum areas allocated to Primary Users (PUs) by dynamically adjusting their settings. However, the spectrum detection subsystem of SUs consumes battery power that could be used for transmission. This work aims to address the energy availability issue for cognitive radio devices by two methods: energy harvesting from the ambient environment and deep learning prediction of future energy levels. We compare three deep learning models: Long-Short Term Memory (LSTM), Convolutional Neural Network (CNN), and Convolutional Long-Short Term Memory (ConvLSTM) with three classic machine learning models: Artificial Neural Networks (ANN), Support Vector Regressor (SVR), and Extreme Gradient Boost (XGBoost). The results show that deep learning models outperform machine learning models across all datasets, with ConvLSTM being the best model with a Normalized Root Mean Squared Error (nRMSE) of 0.0632 and Mean Absolute Error (MAE) of 1.479, which are 8.80% and 9.04% better than the best machine learning model, ANN, with nRMSE of 0.0693 and MAE of 1.626.

show abstract

Section: A Radio Frequency Energy Harvestingmentioning

confidence: 99%