AoI-optimal Joint Sampling and Updating for Wireless Powered Communication Systems

TL;DR

This paper develops an optimal policy for minimizing the Age of Information in wireless powered systems by jointly optimizing sampling, updating, and energy transfer, revealing a threshold-based structure.

Contribution

It analytically characterizes the structure of the AoI-optimal policy in RF-powered systems, incorporating energy and time costs, and demonstrates its superiority over generate-at-will policies.

Findings

The AoI-optimal policy has a threshold-based structure.

Numerical results confirm the analytical structure and impact of system parameters.

Joint optimization improves AoI performance compared to non-optimized policies.

Abstract

This paper characterizes the structure of the Age of Information (AoI)-optimal policy in wireless powered communication systems while accounting for the time and energy costs of generating status updates at the source nodes. In particular, for a single source-destination pair in which a radio frequency (RF)-powered source sends status updates about some physical process to a destination node, we minimize the long-term average AoI at the destination node. The problem is modeled as an average cost Markov Decision Process (MDP) in which, the generation times of status updates at the source, the transmissions of status updates from the source to the destination, and the wireless energy transfer (WET) are jointly optimized. After proving the monotonicity property of the value function associated with the MDP, we analytically demonstrate that the AoI-optimal policy has a threshold-based structure w.r.t. the state variables. Our numerical results verify the analytical findings and reveal the impact of state variables on the structure of the AoI-optimal policy. Our results also demonstrate the impact of system design parameters on the optimal achievable average AoI as well as the superiority of our proposed joint sampling and updating policy w.r.t. the generate-at-will policy.