Non-parametric statistical learning for URLLC transmission rate control

As an important service for 5G communications, ultra-reliable low-latency communications (URLLC) support emerging mission-critical applications, such as factory automation and autonomous driving. For such applications, the probability of failing to successfully transmit URLLC packets should be below a certain threshold. However, in the case of limited knowledge of the channel distribution, achieving such a reliability target requires precise channel modeling. In this paper, we study applying a non-parametric statistical learning approach (i.e. kernel density estimation (KDE)) to estimate the information of the wireless transmission environment (i.e. the probability density function of the channel distribution). Based on the estimated cumulative distribution function, a transmission rate control technique has been developed and the corresponding reliability has been investigated using two measures representing the average performance and the confidence level. Moreover, this paper compares the performance of KDE and traditional empirical estimation scheme. The results show that KDE achieves a high level of confidence in guaranteeing the reliability constraint despite of the limited number of training data when choosing a suitable kernel bandwidth.