Next generation wireless networks must sustain deterministic service levels to support emerging time-sensitive applications. The ability to guarantee bounded latencies depends on the efficient management of radio resources. Several studies propose leveraging the native intelligence of future networks to develop predictive schedulers capable of efficiently managing resources. However, existing proposals focus on semi-static scheduling, where resources are reserved based on traffic predictions, and these reservations are susceptible to inefficiencies due to prediction inaccuracies. This study advances the state of the art with a novel predictive dynamic scheduling scheme that avoids such inefficiencies, and leverages traffic predictions to allocate resources to incoming requests that meet their latency requirements while avoiding resources likely to be needed by future predicted packets. Our results demonstrate that the proposed predictive dynamic scheduling effectively supports deterministic communications in scenarios with mixed traffic flows and varying QoS requirements.

翻译：下一代无线网络必须维持确定性的服务等级以支持新兴的时间敏感应用。保障有界延迟的能力取决于对无线电资源的有效管理。多项研究提出利用未来网络的本地智能开发能够高效管理资源的预测性调度器。然而，现有方案专注于半静态调度——根据流量预测预留资源——这种预留方式因预测误差而易产生效率低下问题。本研究提出一种全新的预测性动态调度方案以突破现有技术瓶颈：该方案避免了上述低效问题，通过利用流量预测为满足延迟需求的到达请求分配资源，同时规避未来预测数据包可能需要的资源。实验结果表明，在混合流量流与差异化服务质量需求场景下，所提出的预测性动态调度能有效支撑确定性通信。