Novel packet scheduling for supporting various real-time IoT applications in LTE networks

Abstract

Recently, LTE networks are attracting a great deal of attention as a platform for real-time IoT applications. Individual devices such as vehicles, drones, and sensors can exchange real-time information with each other on this platform. For example, vehicles periodically provide each other with their real-time location information to avoid automobile collision. 3GPP stipulates that the deadline for vehicle-collision avoidance is 100 msec. In LTE networks, since the throughput of the wireless section fluctuates depending on the wireless channel quality, the time required for each direction differs. To support these various use cases on the platform, it is important to improve the total amount of application data that meets its deadline — we call the metric for this the goodput. However, in LTE networks, conventional MAC schedulers such as the Proportional Fair can obtain only very low goodput when the network load is increased. In this paper, we propose a novel packet scheduling method that adaptively prioritizes each item of application data on the basis of uplink/downlink deadlines and wireless channel quality by adjusting the deadlines. We evaluate our proposed method on NS-3 and find that our proposed method outperforms the conventional Proportional Fair method, which is the most implemented method on eNB.