🤖 AI Summary
This work addresses the challenge of jointly achieving high reliability and energy efficiency in remote threshold-based decision-making over short-packet wireless links, where outage risk complicates the trade-off between timely decisions and power consumption. The authors propose a novel framework that integrates a predictive triggering mechanism with an age-of-information (AoI)-aware elastic update policy. By innovatively coupling prediction-based triggering with AoI control and modeling channel dynamics via a two-state Markov chain, the approach enables joint optimization of decision reliability and energy usage. Through a Bayesian posterior decision rule and co-design of transmit power and update probability, the method significantly advances decision timing at comparable energy costs while maintaining low false-alarm and miss-detection rates, outperforming existing baseline schemes.
📝 Abstract
Remote threshold decisions require more than accurate state estimates: the posterior must support reliable alarm/no-alarm decisions and, when possible, anticipate early critical decisions. We study this problem over short-packet wireless links with outage risk. We derive false-positive/false-negative feasibility conditions that define a decision-feasible region of the estimation and yield a predictive decision-update trigger. To protect predictive updates from outages, we add AoI-controlled resilience updates that both detect disruptions and maintain freshness. A two-state Markov surrogate of the thresholded process, matched to its one-step switching statistics, enables tractable long-term reliability-energy analysis. Then, we jointly optimized transmit power and AoI-controlled resilience update probabilities. Simulations show earlier, reliable decisions at competitive energy with baselines.