Automated method helps researchers quantify uncertainty in their predictions
A new technique can help researchers who use Bayesian inference achieve more accurate results more quickly, without a lot of additional work.
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A new technique can help researchers who use Bayesian inference achieve more accurate results more quickly, without a lot of additional work.
This paper considers energy delivery by a Hybrid Access Point (HAP) to one or more Radio Frequency (RF)-energy harvesting devices. Unlike prior works, it considers imperfect and causal Channel State Information (CSI), and probabilistic constraints that ensure devices receive their required amount of energy over a given planning horizon. To this end, it outlines two novel contributions. The first is a chance-constrained program, which is then solved using a Mixed Integer Linear Program (MILP) coupled with a Sample Average Approximation (SAA) method. The second is a Model Predictive Control (MPC...