Deep reinforcement learning‐based dual‐mode congestion control for cellular V2X environments

Abstract

Abstract The Society of Automotive Engineers (SAE) J2945/1 standard for Dedicated Short Range Communication (DSRC) environmentutilizes transmit (Tx) power control and rate control elements for the periodic BSM transmissions, which are intended to work in a complementary manner. An equivalent standard for the cellular vehicle‐to‐everything (C‐V2X) communication environment is J3161/1, but it eliminates Tx power control and uses only rate control. However, the consequence is the degraded update delay of neighbouring vehicles’ kinematics, potentially undermineing driving safety. In this Letter, the authors propose to retain the dual‐mode control in the C‐V2X environment and find a policy through reinforcement learning (RL) to adjust the rate control function to maintain synergy. Moreover, the authors can extract the RL‐created policy from the neural network so that it can be explicitly specified in the standard, and downloaded and used more conveniently by vehicles. Finally, the RL‐generated policy achieves a better packet delivery frequency than J2945/1 or J3161/1.

Keywords

• artificial intelligence
• cellular radio
• telecommunication congestion control
• vehicular ad hoc networks