Optimizing Sampling for Data Freshness: Unreliable Transmissions with Random Two-way Delay

Jiayu Pan, Ahmed M. Bedewy, Yin Sun, Ness B. Shroff

In this paper, we study a sampling problem, in which freshly sampled data is sent to a remote destination via an unreliable channel, and acknowledgments are sent back on a feedback channel. Both the forward and feedback channels are subject to random transmission times. We optimize the sampling strategy at the source (e.g., a sensor), aiming to enhance the freshness of data samples delivered to the destination (e.g., an estimator). This sampling problem is motivated by a distributed sensing system, where an estimator estimates a signal by combining noisy signal observations collected from a local sensor and accurate signal samples received from a remote sensor. We show that the optimal estimation error is an increasing function of the age of received signal samples. The optimal sampling problem for general non-decreasing age functions is formulated as an MDP with an uncountable state space. An exact solution to this problem is derived, which has a simple threshold-type structure. The threshold can be calculated by low-complexity bisection search and fixed-point iterations. We find that, after a successful packet delivery, the optimal sampler may wait before taking the next sample and sending it out, whereas no waiting time should be added if the previous transmission failed.

Knowledge Graph



Sign up or login to leave a comment