The Best Time for an Update: Risk-Sensitive Minimization of Age-Based
  Metrics
Wanja de Sombre Andrea Ortiz Frank Aurzada Anja Klein
Popular methods to quantify transmitted data quality are the Age of
Information (AoI), the Query Age of Information (QAoI), and the Age of
Incorrect Information (AoII). We consider these metrics in a point-to-point
wireless communication system, where the transmitter monitors a process and
sends status updates to a receiver. The challenge is to decide on the best time
for an update, balancing the transmission energy and the age-based metric at
the receiver. Due to the inherent risk of high age-based metric values causing
complications such as unstable system states, we introduce the new concept of
risky states to denote states with high age-based metric. We use this new
notion of risky states to quantify and minimize this risk of experiencing high
age-based metrics by directly deriving the frequency of risky states as a novel
risk-metric. Building on this foundation, we introduce two risk-sensitive
strategies for AoI, QAoI and AoII. The first strategy uses system knowledge,
i.e., channel quality and packet arrival probability, to find an optimal
strategy that transmits when the age-based metric exceeds a tunable threshold.
A lower threshold leads to higher risk-sensitivity. The second strategy uses an
enhanced Q-learning approach and balances the age-based metric, the
transmission energy and the frequency of risky states without requiring
knowledge about the system. Numerical results affirm our risk-sensitive
strategies' high effectiveness.