Throughput Enhancement in Hybrid Vehicular Networks Using Deep Reinforcement Learning

Cooperative intelligent transportation systems are now being widely investigated along with the emergence of vehicular communication. Services such as collective perception require a robust communication system with high throughput and reliability. However, a single communication technology is unlikely to support the required throughput, especially under mobility and coverage constraints. Thus, we propose in this paper a hybrid vehicular communication architecture that leverages multiple Radio Access Technologies (RATs) to enhance the communication throughput. We developed a Deep Reinforcement Learning (DRL) algorithm to select the optimal hybrid transmission strategy according to the channel quality parameters. We assess the effectiveness of our hybrid transmission strategy by a simulation scenario that shows about 20% throughput enhancement and a 10% reduction of channel busy ratio.