Predictive Resource Allocation with Deep Learning

Assigning radio resources in advance to nonrealtime (NRT) service in a proactive manner can exploit residual resource after serving realtime service to boost the performance of wireless networks. By predicting future average data rate of each mobile user requesting NRT service in a time window, either directly or indirectly, a plan for assigning future resources to each user can be made. Most existing works make the plan by solving optimization problems, which require high computational complexity when the number of users is large and the prediction window in long. In this paper, we design a deep neural network (DNN), which contains an autoencoder and a fully-connected neural network, to learn the resource allocation pattern in a prediction window. With the help of the DNN trained offline, the plan can be made with low complexity. To increase the generalizability to time-varying traffic load for both NRT and realtime services, we resort to selective sampling in active learning. Simulation results show that the proposed method performs closely to the optimal solution in supporting high throughput with given quality of service requirement.