An Adaptive Tree Algorithm to Approach Collision-Free Transmission in Slotted ALOHA

Abstract

A new reinforcement-learning approach is introduced to improve the performance of the slotted ALOHA protocol. Nodes use known periodic schedules as base policies with which they can collaboratively learn how to transmit periodically in different time slots to limit packet collisions. The Adaptive Tree (AT) algorithm is introduced for this purpose, which results in AT-ALOHA. It is shown that nodes using AT-ALOHA quickly converge to transmission schedules that are virtually collision-free, and that the throughput of AT-ALOHA resembles that of TDMA, but without the need to define transmission frames with a given number of time slots. AT-ALOHA is shown to attain better throughput and fairness than slotted ALOHA with exponential back offs and ALOHA-Q (framed slotted ALOHA with Q learning).