Dynamic Payload-Length-Aware Scheduling Strategies for Age Optimization in RF-Powered Ambient Backscatter Networks

Ensuring information freshness in Internet of Things (IoT) networks is essential for real-time monitoring. Due to the limited transmission capability of low-power IoT devices and the rich information content of each status update, a single update may need to be segmented into multiple packets. To achieve timely updates under dynamic wireless and energy conditions, the payload length must be adaptively selected according to the channel state and available energy. This paper investigates an RF-powered ambient backscatter (AmBC) network in which a device alternates between harvesting energy and transmitting status updates. The goal is to jointly optimize energy harvesting, transmission decisions, and payload length to minimize the long-term average age of information (AoI). The problem is formulated as an average-cost Markov decision process. A theoretical lower bound is first derived to characterize the best achievable freshness. Structural analysis further reveals how energy harvesting, battery evolution, channel fading, and adaptive payload jointly determine the optimal scheduling policy and shows that it follows a threshold-based structure. Leveraging this property, we develop a low-complexity structure-aware value-iteration-based method suitable for low-power AmBC devices with limited computational capability, and further propose two scalable model-free solutions based on Q-learning and deep Q-networks to support online scheduling when model knowledge is unavailable or the state space becomes large. Simulation results show that the proposed algorithms outperform benchmark schemes and validate the analytical structure and properties of the optimal policy.