Age-Gain-Dependent Random Access for Event-Driven Periodic Updating

Abstract

This paper considers utilizing the knowledge of age gains to reduce the average age of information (AoI) in random access with event-driven periodic updating for the first time. Built on the form of slotted ALOHA, we require each device to determine its age gain threshold and transmission probability in an easily implementable decentralized manner, so that the contention can be limited to devices with age gains as high as possible. For the basic case that each device utilizes its knowledge of age gain of only itself, we provide an analytical modeling by a multi-layer discrete-time Markov chains (DTMCs), where an external DTMC manages the jumps between the beginnings of frames and an internal DTMC manages the evolution during an arbitrary frame, for obtaining optimal fixed access parameters offline. For the enhanced case that each device utilizes its knowledge of age gains of all the devices, we require each device to adjust its access parameters for maximizing the estimated network expected AoI reduction per slot, through maintaining the a posteriori joint probability distribution of local age and age gain of an arbitrary device in a Bayesian manner. Numerical results validate our study and demonstrate the advantage of the proposed schemes over other schemes.