# Artificial Intelligence-Based Discontinuous Reception for Energy Saving in 5G Networks
## 4 Performance Analysis
The generated data packets are collected in the buffer and served to the UE during the active event. UE checks the buffer during ON period, in case of any packet in buffer, the UE switches to the active event, or else continues to sleep. During an active event, the buffered packets are served to UE after getting the beam pairs alignment between UE and gNB. At the same time, packet arrival time is inputted to the trained LSTM model to predict the upcoming packet arrival time. We can obtain the dynamic sleep duration by subtracting the previous packet arrival time value from the predicted dynamic time value \(T_{DY}\) (upcoming packet arrival time). Figure 8: Prediction result and root mean square error (RMSE) for first test set. Figure 9(a),b, demonstrate the energy efficiency and mean delay with varying ON Period (\(T_{ON}\)) for AI-DRX and LTE-DRX over trace 1 & trace 2, respectively. In Figure 10, \(T_{ON}\) is varied from 1 ms to 160 ms while the values for \(Th_{Min}=20\) & \(Th_{Max}=100\) are considered. Figure 9(a) highlights the decrease in energy efficiency with increase in \(T_{ON}\). The reason for the drop in energy efficiency is, UE waits for longer period in ON state prior switching to active state (to serve packets). It can be seen from Figure 9(b), the mean delay for trace 1 ranges from 150 ms to 194 ms with an increase in ON period. The reason for rise in mean delay lies in the feet that with an increase in ON duration, the time spend by UE in ON state will be higher, which results in higher delay. Moreover, by selecting the optimum value of \(T_{ON}\), delay observed by UE can be minimized. From Figure 9(a), it is noticed that the energy efficiency of trace 2 is higher than that of trace 1 due to higher arrival rate of trace 1 as compared to trace 2. We have compared the performance of AI-DRX with LTE-DRX. To implement the LTE-DRX, we select the value of short sleep cycle to \(Th_{Min}\), the value of long sleep cycle to \(Th_{Max}\) and fed trained model with trace 1 and trace 2. It is observed in Figure 9(a) AI-DRX energy efficiency for trace 1 is 69% higher than that of LTE-DRX, at the cost of higher delay. The reason arises from the fact that AI-DRX  
Figure 9: Prediction results and RMSE for random test sets. calculates sleep time form real wireless traffic trace based on arrival rate, while LTE-DRX uses \(Th_{Min}\) and \(Th_{Max}\) to set the short and long sleep time. For AI-DRX, the small value of \(Th_{Min}\) and \(Th_{Max}\) achieves higher energy efficiency as UE easily transits to long sleep time, which results in a larger delay. Whereas, for small values of \(Th_{Min}\) and \(Th_{Max}\) in LTE-DRX, a UE sleeps for a short period that results in less energy efficiency and smaller mean delay. AI-DRX achieves 55% higher energy efficiency as compared to that of LTE-DRX using trace 2. Figures 11 and 12 present the energy efficiency and mean delay with varying ON period \(T_{ON}\) for \(Th_{Min}=200\) & \(Th_{Max}=1000\) and \(Th_{Min}=300\) & \(Th_{Max}=1600\), respectively. It is observed from Figures 11 and 12 that the energy efficiency and mean delay of LTE-DRX increase with an increase in \(Th_{Min}\) & \(Th_{Max}\), as UE sleep longer. The energy efficiency of AI-DRX decreases with an increase in \(Th_{Min}\) & \(Th_{Max}\), as UE will not able to transit to sleep state if \(T_{DY}<Th_{Min}\). With a small value of sleep time \(T_{DY}\) or higher value of \(Th_{Min}\) & \(Th_{Max}\), UE remains in the active state, which results in less energy efficiency. To validate our proposal, we have compared our work to traditional Poisson arrival model. We generated three data sets considering Poisson arrival rate (\(\lambda\)) with mean value of \(\lambda=1/20\), \(\lambda=1/10\), and \(\lambda=1\). We have trained model using Poisson arrival rate. The generated traces are fed to AI-DRX algorithm to analyze the energy efficiency and mean delay. Figure 12(a),b shows the energy efficiency and mean delay for AI-DRX trace 2 and Poisson arrival with varying \(T_{ON}\).