Deep‐DRX: A framework for deep learning–based discontinuous reception in 5G wireless networks

The tremendous advancement in various types of mobile devices with distinct services demands emerging 5G networks to deal with different kinds of traffic. The high data rates along with beam searching operation in 5G increase user equipment (UE) energy expenses. Discontinuous reception (DRX) is used in long‐term evolution network to save the power of UE. The DRX conserves the energy at the cost of increased latency. On the other hand, long short‐term memory (LSTM), a deep neural network, has shown incredible results in learning time‐varying sequences from data sets. In this article, we propose a novel idea to train LSTM network for prediction of next packet arrival time based on real wireless traffic trace (dataset). Then, we use the trained LSTM model to predict dynamic sleep time in DRX for 5G networks. Our proposed algorithm, deep learning–based DRX (Deep‐DRX) is able to make dynamic sleep cycle in 5G communications. Deep‐DRX achieves 10% and 30% of power saving with a mean delay of 1.006 ms and 1.05 ms for Trace 1 and Trace 2, respectively. In this article, we presented LSTM‐based DRX for 5G communications. We train LSTM network for prediction of next packet arrival time based on real wireless traffic trace (dataset). We used the trained LSTM model to predict dynamic sleep time in DRX for 5G networks. Our proposed algorithm, deep learning‐based DRX (Deep‐DRX) enables UE to adopt the dynamic sleep cycles based on the current packet interarrival time values.