Distributed Timeslot Allocation in mMTC Network by Magnitude-Sensitive Bayesian Attractor Model

In 5G, flexible resource management, mainly by base stations, will enable support for a variety of use cases. However, in a situation where a large number of devices exist, such as in mMTC, devices need to allocate resources appropriately in an autonomous decentralized manner. In this paper, autonomous decentralized timeslot allocation is achieved by using a decision model for each device. As a decision model, we propose an extension of the Bayesian Attractor Model (BAM) using Bayesian estimation. The proposed model incorporates a feature of human decision-making called magnitude sensitivity, where the time to decision varies with the sum of the values of all alternatives. This allows the natural introduction of the behavior of making a decision quickly when a time slot is available and waiting otherwise. Simulation-based evaluations show that the proposed method can avoid time slot conflicts during congestion more effectively than conventional Q-learning based time slot selection.