Year: 2020
Type: conference-paper
Abstract: Slotted ALOHA is known to have poor channel utilization (a maximum of 37% when average offered load is one packet per time slot). Reinforcement learning has recently been proposed as a technique that allows nodes to learn to coordinate their transmissions in order to attain much higher network utilization. All reinforcement learning schemes proposed to date assume immediate feedback on the outcome of a packet transmission. We introduce ALOHA-dQT, a reinforcement-learning protocol that achieves high utilization by having nodes broadcast short summaries of the channel history as known to them along with their packets. Our simulation results show that ALOHA-dQT leads to network utilization above 75%, with fair bandwidth allocation among nodes. ALOHA-dQT is the first reinforcement-learning approach applied to slotted ALOHA suitable for ad-hoc networks without centralized repeaters.
Authors: Molly Zhang, Luca de Alfaro, J.J. Garcia-Luna-Aceves
Institution: University of California, Santa Cruz
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W3110351545