@article{M67FCDFB0,
title = "Spiking Neural Network-Based Backoff Scheme for Slotted-ALOHA Systems",
journal = "The Journal of Korean Institute of Communications and Information Sciences",
year = "2024",
issn = "1226-4717",
doi = "10.7840/kics.2024.49.2.199",
author = "Min Jeong Kang, Jung Hoon Lee, Dong Geun Jeong",
keywords = "Backoff scheme, machine learning, slotted-ALOHA, spiking neural network",
abstract = "In this letter, we propose a spiking neural network (SNN)-based backoff scheme to enhance the performance of slotted-ALOHA systems, which operates with relatively low power and implementation complexity. In our scheme, an SNN model takes real-time input of observation data, such as transmission success and failure, from the system operating with binary exponential backoff (BEB), and returns the optimal contention window size. Through simulations, we show that our proposed scheme achieves higher performance compared to the traditional BEB, approaching the performance of the optimal backoff scheme numerically optimized."
}