# Artificial Intelligence-Based Discontinuous Reception for Energy Saving in 5G Networks
## 1 Introduction  
The use of cellular gadgets, like smartphones, notebooks, and tablets has comforted our life. The Ericsson mobility report predicts the rise of cellular traffic to 8.8 billion by 2024 [1]. These extensive growing cellular users require improved data rates with heterogeneous services in next generation networks. 5G expects to deal with various types of traffics including periodic and delay tolerant traffic for IoT devices or burst type of traffic for delay intolerant services [2, 3]. 3 rd Generation Partnership Project (3 GPP) planned the standardization of GHz spectrum (mm-wave) to address the users' demand of high bandwidth. However, communication over high-frequency bands of the mm-wave requires directional air interface and narrower beams to reduce the path loss. In directional air interface, UE has to search for best beam pairs and make adequate beam alignment with the next generation nodeB (gNB) [4]. In addition to beam searching process in 5G networks, massive MIMO, higher order modulation schemes and advanced coding techniques also increase UE energy expenses. Long term evolution (LTE) networks utilize DRX mechanism to reduce the energy consumption of the UE [2; 5]. DRX enables a UE to save energy by switching off the radio circuitry part, in case of no incoming data. LTE-DRX turns off radio part during long and short sleep cycles in order to reduce the energy consumption of UE. The sleep cycles in LTE-DRX are of the fixed time period. If any new packet arrives during the fixed time sleep cycle, the packets will be buffered at evolved NodeB (eNB). The eNB serves the stored packets after the completion of each sleep cycle in LTE-DRX. Hence, the LTE-DRX saves the UE energy at the cost of delay [6; 7]. LTE-DRX in its current form is not suitable for energy savings in 5G enabled cellular devices [8; 9], due to two main reasons. * The communications in LTE do not consider beamforming, whereas in 5G networks UE has to align the best beam pair before the start of communications. * The LTE-DRX mechanism has a fixed length of sleep duration, which increases the energy consumption and delay. Hence, LTE-DRX is not suitable for low latency communications in 5G networks. Authors in [3] propose DRX for 5G network, which requires UE to search for best beam pairs after completion of each sleep cycle in order to serve the packets. The additional beam searching operation after each sleep cycle enhances the energy consumption of UE. The work in [10] suggests beam aware DRX approach for 5G enabled machine to machine communications, in which UE has prior information about best beam pairs. Similarly, beam aware DRX mechanism for energy saving in the 5G network is proposed in [11]. The beam aware mechanism may not perform well in case of UE mobility and beam misalignment [12]. [PERSON] et al. [12] show that the probability of beam misalignment increases with an increase in UE velocity. The probability of misalignment is 0.1 and 0.38 if a UE moves with the velocity of 30 Km/h and 60 Km/h, respectively. The DRX with built-in state of beamforming is proposed by [PERSON] et al. [13]. Authors present the concept of DRX for multiple beam communications and utilize the semi-Markov model to design eleven-states of DRX. Their approach considers the beam training process only in case of beam misalignment and after completion of long sleep duration. Hence, authors save the energy of UE while minimizing the delay for 5G services. However, the fixed duration of sleep cycles in their approach may cause more energy consumption. On the other hand, Recurrent Neural Network (RNN) in AI has shown incredible results to predict the upcoming value of a given sequence [14]. Long Short-Term Memory (LSTM) is a popular type of RNN that is specially designed to learn long-term dependencies of a sequence for predicting the upcoming value of a sequence [15].