Novel QoS Control Framework for Automotive Safety Related and Infotainment Services
Autonomous driving will rely on a multitude of connected applications with stringent quality of service (QoS) requirements in terms of low latency and high reliability. At the same time, passengers relieved of steering duty have the opportunity to enjoy infotainment services that are often associated with high data rates, e.g. video streaming. The simultaneous usage of such safety related and infotainment services leads to diverse QoS requirements which are difficult to satisfy in current wireless networks. In an effort to address this issue, we propose a two layer predictive resource allocation framework that leverages the services' properties and incomplete channel information. First, we optimize packet transmission times by a so called statistical look ahead scheduling to enhance the network's QoS and spectral efficiency based upon channel distribution information. Second, packets are forwarded to an online scheduler according to the outcome of this first optimization. Physical resources are assigned considering the services' QoS requirements and current channel state. We present a novel heuristic that performs real time resource assignment. Simulations show that our approach has a potential for improving transmission reliability and spectral efficiency.
2020 IEEE Wireless Communications and Networking Conference (WCNC)