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Reinforcement learning for improved UAV-based integrated access and backhaul operation
- Source :
- ICC Workshops
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- There is a strong interest in utilizing commercial cellular networks to support unmanned aerial vehicles (UAVs) to send control commands and communicate heavy traffic. Cellular networks are well suited for offering reliable and secure connections to the UAVs as well as facilitating traffic management systems to enhance safe operation. However, for the full-scale integration of UAVs that perform critical and high-risk tasks, more advanced solutions are required to improve wireless connectivity in mobile networks. In this context, integrated access and backhaul (IAB) is an attractive approach for the UAVs to enhance connectivity and traffic forwarding. In this paper, we study a novel approach to dynamic associations based on reinforcement learning at the edge of the network and compare it to alternative association algorithms. Considering the average data rate, our results indicate that the reinforcement learning methods improve the achievable data rate. The optimal parameters of the introduced algorithm are highly sensitive to the donor next generation node base (DgNB) and UAV IAB node densities, and need to be identified beforehand or estimated via a stateful search. However, its performance nearly converges to that of the ideal scheme with a full knowledge of the data rates in dense deployments of DgNBs. acceptedVersion
- Subjects :
- business.industry
Computer science
213 Electronic, automation and communications engineering, electronics
05 social sciences
050801 communication & media studies
Advanced Traffic Management System
Backhaul (telecommunications)
0508 media and communications
Stateful firewall
0502 economics and business
Cellular network
Reinforcement learning
050211 marketing
business
Computer network
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- ICC Workshops
- Accession number :
- edsair.doi.dedup.....48b9bb01a2d3ec6e4f4fe522dccf192e