A General and Energy-Efficient Message Delivery Scheme With M3RSMA for Complex Intersections

At complex intersections, it has become a consensus to address the perception limitations (such as blind spots, the difficulty of accurately obtaining road condition information in distant areas or under adverse weather conditions) faced by single-vehicle intelligence through vehicle-infrastructure cooperation. However, under the constraints of limited spectrum resources, the sharply rising in the number of connected vehicles, and the pressing requirement for effective utilization of electric power, there has been no study on how to design a scheme that assists roadside unit (RSU) completing roadside cooperative message delivery (RCMD) with minimal transmit power. Towards this end, this paper proposes a general and energy-efficient RCMD scheme based on multicarrier multigroup multicasting rate-splitting multiple access. We formulate a joint optimization problem involving the RSU's transmit power, along with the power and size coefficients of each message. Then we design a deep reinforcement learning-assisted bilevel resource allocation algorithm to solve this problem. Finally, we collect extensive numerical results, using the message delivery success probability and RSU's transmit power as performance metrics, from the proposed scheme and multiple benchmarks. Results indicate that the proposed scheme not only exhibits strong adaptability (i.e., it can be deployed at any intersection regardless of the number of lanes, vehicles, or traffic complexity), but also significantly reduces the RSU's transmit power while maintaining high message delivery success probability.