Lightweight Energy Budget Prediction for Wireless Transceivers

In this study, we propose a generalizable approach for predicting the energy budget to sustain wireless communication in multiple scenarios. We achieve this by using predictive models to estimate the duration of transceiver modes (receive, idle, and transmit) in a future window. We applied our approach to distinct scenarios to evaluate its generalization capabilities. Through these experiments, we demonstrated the versatility of our approach and its ability to produce lightweight predictive models suitable for distinct applications. Our best-performing model, Multi-layer Perceptron (MLP), achieved a prediction error of less than 6 % across all scenarios, with an energy overhead of under 8 μ<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$J$</tex> and an inference time of less than 3.5 μs when embedded on a RISC-V single board computer. These results validate the effectiveness and practicality of our approach for predicting communication energy budgets in resource-constrained environments.