Frame slotted ALOHA (FSA) has been thede factomultiple-access protocol for many energy-efficient Internet of Things applications. To improve the age of information (AoI) that measures the freshness of the status update, we devote this paper to designing an age-threshold FSA protocol that adaptively limits the contention in each frame to users with age gains as high as possible, by focusing on a multiple-packet reception (MPR) physical layer model of practical importance for the first time. For an ideal scenario where the coordinator always knows the exact age gain of each user, we propose a low-complexity algorithm to approximate the optimal age gain threshold and frame length for maximizing the expected slot-average AoI reduction within the upcoming frame, which provides a design clue for other scenarios. With this clue, for a practical scenario where the coordinator has to estimate the age gains based on its feasible observations, we design a Bayesian method to update individual distributions of the local ages of all the users based on both the channel statuses and the AoI of each user, and propose an algorithm to approximate optimal access parameters based on these distributions. We also evaluate the computational complexity of the proposed practical scheme and discuss how to generalize it to consider random channel errors. Numerical experiments show that our proposed practical scheme outperforms state-of-the-art schemes for a wide range of MPR configurations.

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.