DeepMPR: Enhancing Opportunistic Routing in Wireless Networks through Multi-Agent Deep Reinforcement Learning
Year: 2023
Type: preprint
Abstract: Opportunistic routing relies on the broadcast capability of wireless networks. It brings higher reliability and robustness in highly dynamic and/or severe environments such as mobile or vehicular ad-hoc networks (MANETs/VANETs). To reduce the cost of broadcast, multicast routing schemes use the connected dominating set (CDS) or multi-point relaying (MPR) set to decrease the network overhead and hence, their selection algorithms are critical. Common MPR selection algorithms are heuristic, rely on coordination between nodes, need high computational power for large networks, and are difficult to tune for network uncertainties. In this paper, we use multi-agent deep reinforcement learning to design a novel MPR multicast routing technique, DeepMPR, which is outperforming the OLSR MPR selection algorithm while it does not require MPR announcement messages from the neighbors. Our evaluation results demonstrate the performance gains of our trained DeepMPR multicast forwarding policy compared to other popular techniques.
Source: arXiv (Cornell University)
Authors: Kaviani, Saeed, Ryu, Bo, Ahmed, Ejaz, Kim, Deokseong, Kim, Jae, Spiker, Carrie, Harnden, Blake
Institutions: EpiSys Science (United States), Boeing (United States)
Language: English
Topic: Mobile Ad Hoc Networks
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Office of Naval Research
Award: G8876996369
OpenAlex ID: W4381247951