An M3RSMA-Based Roadside Cooperative Message Delivery Scheme for Complex Intersection

Traditional single-vehicle intelligence system faces challenges such as blind spots and perception performance bottlenecks due to limitations in sensor perception angles, ranges, and accuracy, which are particularly pronounced in complex intersection. Vehicle-infrastructure cooperative mechanism has been widely recognized as a promising solution to address challenges faced by single-vehicle intelligence. However, against the backdrop of limited spectrum resources and the sharply rising in the number of connected vehicles, how to efficiently deliver cooperative messages from roadside unit to vehicles is often overlooked. Towards this end, we propose a roadside cooperative message delivery scheme based on multicarrier multigroup multicast rate-splitting multiple access, considering the rarely explored case of transmitting messages with limited size under delay constraint. Then we focus on the critical joint optimization problem of message size and power allocation, with consideration for imperfect channel state information at the transmitter. Subsequently, a multi-agent deep reinforcement learning based resource allocation algorithm is designed to solve this joint optimization problem, exhibiting robustness to dynamic changes in vehicle density and message size. Finally, we analyze through extensive numerical results the impacts of various factors on message delivery success probability.