Interplay between VAoI, Packet Error Rate, and Delay for Energy-Efficient Remote Monitoring
Abstract
This letter studies energy optimization of short-packet transmission for event-triggered remote monitoring over finite-blocklength wireless links. A wireless sensor node generates updates only when the source state changes, and freshness is measured by the Version Age of Information (VAoI). We model the VAoI evolution as a Markov chain and show its coupling with the packet error rate, characterized by decoding error probability, and average delay. Then, we formulate a transmit-power allocation problem that minimizes the long-term average energy consumption under a VAoI constraint and solve it using a low-complexity search method. Numerical results show that the update arrival probability and blocklength strongly affect the energy--VAoI tradeoff, and that optimizing long-term energy consumption can substantially reduce energy compared with minimizing the energy per transmission.
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BibTeX
@misc{khorsandmanesh2026interplay,
title = {{Interplay between VAoI, Packet Error Rate, and Delay for Energy-Efficient Remote Monitoring}},
author = {Yasaman Khorsandmanesh and Zinat Behdad and Emil Bjornson and Cicek Cavdar},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2606.25566},
eprint = {2606.25566},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.25566},
}
RIS
TY - UNPB
TI - Interplay between VAoI, Packet Error Rate, and Delay for Energy-Efficient Remote Monitoring
AU - Yasaman Khorsandmanesh
AU - Zinat Behdad
AU - Emil Bjornson
AU - Cicek Cavdar
PY - 2026
DA - 2026/10/07
PB - arXiv
DO - 10.48550/arxiv.2606.25566
UR - https://arxiv.org/abs/2606.25566
AB - This letter studies energy optimization of short-packet transmission for event-triggered remote monitoring over finite-blocklength wireless links. A wireless sensor node generates updates only when the source state changes, and freshness is measured by the Version Age of Information (VAoI). We model the VAoI evolution as a Markov chain and show its coupling with the packet error rate, characterized by decoding error probability, and average delay. Then, we formulate a transmit-power allocation problem that minimizes the long-term average energy consumption under a VAoI constraint and solve it using a low-complexity search method. Numerical results show that the update arrival probability and blocklength strongly affect the energy--VAoI tradeoff, and that optimizing long-term energy consumption can substantially reduce energy compared with minimizing the energy per transmission.
ER -