Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning
Intel (United States) · Georgia Institute of Technology
Abstract
This paper investigates the spectrum sharing problem in vehicular networks based on multi-agent reinforcement learning, where multiple vehicle-to-vehicle (V2V) links reuse the frequency spectrum preoccupied by vehicle-to-infrastructure (V2I) links. Fast channel variations in high mobility vehicular environments preclude the possibility of collecting accurate instantaneous channel state information at the base station for centralized resource management. In response, we model the resource sharing as a multi-agent reinforcement learning problem, which is then solved using a fingerprint-based deep Q-network method that is amenable to a distributed implementation. The V2V links, each acting as an agent,…
Citation impact
- FWCI
- 28.69
- Percentile
- 100%
- References
- 42
Authors
3Topics & keywords
- Reinforcement learning
- Computer science
- Payload (computing)
- Q-learning
- Reuse
- Computer network
- Distributed computing
- Resource management (computing)
- Industry, innovation and infrastructure