A significant limitation of the LTE-V2X and NR-V2X sidelink scheduling mechanisms is their difficulty coping with variations in inter packet arrival times, also known as aperiodic packets. This conflicts with the fundamental characteristics of most V2X services which are triggered based on an event. e.g. ETSI Cooperative Awareness Messages (CAMs) - vehicle kinematics, Cooperative Perception Messages (CPMs) - object sensing and Decentralised Event Notification Messages (DENMs) - event occurrences. Furthermore, network management techniques such as congestion control mechanisms can result in varied inter packet arrival times. To combat this, NR-V2X introduced a dynamic grant mechanism, which we show is ineffective unless there is background periodic traffic to stabilise the sensing history upon which the scheduler makes it decisions. The characteristics of V2X services make it implausible that such periodic application traffic will exist. To overcome this significant drawback, we demonstrate that the standardised scheduling algorithms can be made effective if the event triggered arrival rate of packets can be accurately predicted. These predictions can be used to tune the Resource Reservation Interval (RRI) parameter of the MAC scheduler to negate the negative impact of aperiodicity. Such an approach allows the scheduler to achieve comparable performance to a scenario where packets arrive periodically. To demonstrate the effectiveness of our approach, an ML model has been devised for the prediction of cooperative awareness messages, but the same principle can be abstracted to other V2X service types.

翻译：LTE-V2X和NR-V2X侧链路调度机制的一个显著局限性在于难以应对数据包到达间隔时间的波动，即非周期性数据包问题。这与大多数V2X服务基于事件触发的核心特征相矛盾（例如，ETSI协作感知消息（CAM）-车辆运动学信息、协作感知消息（CPM）-目标检测以及分散式事件通知消息（DENM）-事件发生）。此外，网络管理技术（如拥塞控制机制）也可能导致数据包到达间隔时间的变化。为此，NR-V2X引入了动态授权机制，但我们的研究表明，除非存在背景周期性流量以稳定调度器决策所依赖的感知历史记录，否则该机制效果不佳。由于V2X服务的特性，此类周期性应用流量存在的可能性极低。为克服这一显著缺陷，我们证明：若能准确预测事件触发的数据包到达速率，标准化调度算法即可发挥有效作用。这些预测可用于调整MAC调度器的资源预留间隔（RRI）参数，从而消除非周期性带来的负面影响。该方法使调度器能够实现与周期性数据包到达场景相当的性能。为验证方法的有效性，我们设计了一个用于预测协作感知消息的机器学习模型，但相同原理可推广至其他V2X服务类型。