Packet Delivery Impact of Predictive Resource Allocation for Quasi-Periodic Cellular V2X Communication

The channel width for Intelligent Transportation System (ITS) band for vehicle-to-everything (V2X) system is expected to be relatively narrow. It implies that effective congestion control mechanisms will be essential. A well-established standard mechanism is the Cooperative Awareness Message (CAM) generation rule that drops unnecessary periodic beacons. Dropping packets from a periodic packet stream produces a non-periodic traffic. It renders the distributed Semi-Persistent Scheduling (SPS) algorithm exposed to resource waste and packet collision problems. The problems can be tackled by supplementing SPS with machine learning (ML) that predicts the transmission times of non-dropped CAM packets and reserves a transmit resource at the predicted time. The proposed approach is shown to significantly extend the distance up to which the target packet reception ratio (PRR) is met, compared to the current SPS algorithm.