Abstract
The 6G space-air-ground integrated network can achieve global full-area three-dimensional coverage and ultra-wide-area broadband access capabilities anytime and anywhere. Through the integration of satellite networks and terrestrial networks, it can provide a better user experience and has become the core development direction of 6G networks. Inspired by the potential of service differentiation ability of 6G IEEE 802.11ax protocols (Wi-Fi 6), a Data-driven Fair-Hierarchical Scheduling (DFHS) is proposed in this paper to schedule packets from diversified applications in dense Internet of Things (IoT) networks, which allows data streams to fairly share the spectrum and at the same time satisfy delay requirements. Our DFHS is divided into the outer-layer and inner-layer schedulers. First, diversified packets of IoT services are classified into categories according to delay requirement and transmission frequency. Then the outer-layer scheduler assigns classified packets to different Access Categories (AC) with differentiated channel competition capabilities. Next, the inner-layer scheduler optimally schedules the AC queues according to the packet access response ratio. Particle Swarm Optimization algorithm is further introduced to solve the optimal packet scheduling problem. Numerical results demonstrate that the DFHS outperforms the classical FCFS, the latest EARS and Tian's schemes in terms of service missing ratio, queueing delay and fair index.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call