Impact of Prediction Errors on High Throughput Predictive Resource Allocation

Predictive resource allocation (PRA) can leverage machine learning and historical data to boost the performance of mobile networks, say to support high throughput or improve the quality of service. While the large gain of PRA has been demonstrated by assuming perfect prediction or by optimizing with modeled prediction errors, how the prediction errors translate to the performance loss of PRA and how the future information should be predicted are not well understood. In this paper, we consider high throughput PRA policy for non-real-time (NRT) mobile users when the base stations also serve real-time (RT) users, where the future average rates of NRT users in a prediction window need to be predicted. The average rate can be predicted in various ways, where one typical way is to predict the rate directly from the historical average rates and another typical way is to predict the rate indirectly by first predicting traffic load and user trajectory. Given that the traffic load and user location can be gathered easily in cellular networks, we consider such an indirect prediction and analyze the impact of prediction errors of traffic load and trajectory on the prediction errors of average data rate. To this end, we first find the relation between the RT traffic load and the residual bandwidth after serving the RT users by resorting to effective capacity and effective bandwidth theory. To further justify why we analyze the indirect prediction, we compare the predictability of the time series used for the typical direct prediction and indirect prediction with entropy theory. Since different machine learning techniques lead to different prediction bias and prediction error variance, we then derive the relation between the prediction error statistics of the average data rate and those of traffic load and trajectory. We evaluate the performance loss of PRA using both modeled prediction errors and real predictions with deep learning. Our analyses show that for a network busy with RT service, the prediction errors of RT traffic load have larger impact on those of average data rate than the prediction errors of trajectory. The prediction bias has a larger impact on the throughput loss of PRA than the prediction error variance. This provides a guidance of designing learning techniques for predicting the required information.