Title: A TDMA protocol with reinforcement learning slot selection for MANETs

Authors: Chih-Yu Lin; Chih-Hsiang Wang; Yu-Chee Tseng

Addresses: Department of Computer Science and Engineering, National Taiwan Ocean University, Taiwan ' Department of Computer Science, National Chiao Tung University, Taiwan ' Department of Computer Science, National Chiao Tung University, Taiwan; College of Health Sciences, Kaohsiung Medical University, Taiwan

Abstract: With the rapid development of wireless communication and the advantage of infrastructure-less technologies, mobile ad hoc networks (MANETs) have attracted great attention on military and rescue applications. Medium access control is an important issue in MANETs. Contention-based MAC protocols (e.g., CSMA) do not ensure a reliable transmission due to the possibility of collisions. On the contrary, schedule-based MAC protocols (e.g., TDMA) can solve the collision problem with a scheduled transmission plan. However, under an infrastructure-less environment, it is non-trivial for each node to determine its own transmission plan. This work investigates how to use reinforcement learning (RL) to help nodes determine their transmission plans in a TDMA protocol. More precisely, we design a cross-layer TDMA protocol with a RL-based slot selection algorithm. We have validated the proposed protocol by the ns-3 network simulator.

Keywords: medium access control; MAC; mobile ad hoc networks; MANETs; ns-3; reinforcement learning; slot assignment; time division multiple access; TDMA.

DOI: 10.1504/IJAHUC.2021.115123

International Journal of Ad Hoc and Ubiquitous Computing, 2021 Vol.37 No.1, pp.16 - 25

Received: 03 Dec 2020
Accepted: 10 Dec 2020
Published online: 18 May 2021 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article