🤖 AI Summary
This study addresses energy efficiency optimization for short-packet transmission in event-triggered remote monitoring over finite-blocklength wireless links. It presents the first joint modeling framework that integrates information freshness—quantified by Version Age of Information (VAoI)—decoding error probability, and transmission delay to formulate a long-term average energy minimization problem under power allocation constraints. By characterizing the dynamic evolution of VAoI through a Markov chain and leveraging finite-blocklength channel coding theory, the authors develop a low-complexity search algorithm to solve the VAoI-constrained optimization problem. Numerical results demonstrate that the proposed approach significantly outperforms strategies minimizing per-transmission energy consumption alone, achieving an effective trade-off between energy expenditure and information freshness across varying update arrival probabilities and blocklengths, thereby substantially reducing long-term system energy consumption.
📝 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.