# Artificial Intelligence-Based Discontinuous Reception for Energy Saving in 5G Networks
## 3 AI-DRX for Multiple Beam Communications
### AI-DRX for Enabling Dynamic Long and Short Sleep Cycles  
Algorithm 1 (AI-DRX) demonstrates the use of artificial intelligence in the implementation of DRX for 5G networks. AI-DRX makes dynamic short and long sleep cycles in DRX. AI-DRX utilizes trained LSTM model to predict the upcoming value of packet arrival time and subsequently to enable dynamic sleep cycles in DRX. The training process is conducted offline on two traces of real wireless traffic acquired from the University of Massachusetts (UMass) trace repository [27] and Crawdad data set repository [28]. Training process learns the packet arrival time pattern of increasing sequence values from both traces. Once the LSTM network is trained offline with least prediction error, the trained model predicts the upcoming packet time of real wireless traffic. AI-DRX algorithm calculates the dynamic sleep cycles by using prediction results of upcoming packet arrival time value. AI-DRX enables a UE to reduce power consumption in multiple beam communications scenario of 5G networks. AI-DRX saves energy by enabling dynamic short and long sleep cycles, respectively. AI-DRX enables dynamic short sleep cycles or dynamic long sleep cycles based on two threshold values \(Th_{Min}\) and \(Th_{Max}\). The values of \(Th_{Min}\) and \(Th_{Max}\) can be varied according to delay requirements of various services. We discuss three cases of AI-DRX: (1) dynamic short sleep cycles; (2) dynamic long sleep cycles; (3) dynamic inactivity timer. During an active time of AI-DRX, the packets are served to UE. The trained LSTM model predicts the value of upcoming packet arrival time (\(T_{DY}\)) while serving the packets. UE checks for new packets in the buffer via PDCCH during ON state of AI-DRX. If no new packet is observed in the buffer, UE continues to sleep up to \(T_{DY}\). However, if a new packet is observed in the ON state, UE transits to active time and receives the packet(s). Meanwhile, inactivity timer in the active mode is restarted on the reception of every new packet. Furthermore, AI-DRX also considers the case of the active state having empty buffer, the UE counts down for dynamic inactivity timer (third case below) to complete and then transits to sleep cycle for predicted sleep time (\(T_{DY}\)). The first case enables dynamic short sleep cycle if the value of \(T_{DY}\) is greater than or equal to \(Th_{Min}\) and less than \(Th_{Max}\). The condition of dynamic short sleep cycle gets satisfied and UE sleeps for the predicted time value of \(T_{DY}\). There are very small chances of beam misalignment after a short sleep period [12; 13]. Hence, AI-DRX considers the beam training process after dynamic short sleep cycles only if beams are misaligned. Figure 6 shows the timing diagram of dynamic short sleep cycle using AI-DRX algorithm. The second case considers the dynamic long sleep cycle up to the predicted value of \(T_{DY}\), if \(T_{DY}\) is greater than \(Th_{Max}\). The concept of the dynamic long sleep cycle is depicted in Figure 7. The dynamic long sleep cycle saves more energy than that of short sleep cycle. Moreover, there are more chances of beam misalignment after the dynamic long sleep cycles. Hence, AI-DRX performs beam training and feedback after completion of each dynamic long sleep cycle. The third case deals with dynamic inactivity timer. If the predicted value of \(T_{DY}\) is less than \(Th_{Min}\), the UE will remain active until the period \(T_{DY}\) or any new packet arrives. The concept of dynamic inactivity timer can be seen in Figures 6 and 7. AI-DRX also addresses the problem of beam misalignment during active time. AI-DRX performs beam training and feedback in case of any beam misalignment during active time. It may be noted that AI-DRX utilizes dynamic short and long sleep cycles instead of static fixed time sleep cycles. Moreover, the inactivity timer value is also dynamic according to the prediction results (using trained LSTM model).