# Artificial Intelligence-Based Discontinuous Reception for Energy Saving in 5G Networks
## 4 Performance Analysis  
We use MATLAB 2019a for training and testing of RNN on two different traces (data sets). These traces of burst traffic type are taken from the Crawdad dataset repository (trace 1) [28] and UMass trace repository (trace 2) [27]. The trace 2 shows the traffic pattern of HTTP and video streaming applications. The video streaming trace is used as it is expected by 2024 over three-quarters of mobile data traffic will be video traffic [1]. The trace 1 and trace 2 include seven parameters as shown in [11]. These parameters are: (1) serial number; (2) packet arrival time in seconds; (3) source address of the packets; (4) destination address of the packet; (5) protocol used; (6) length of packets in bytes; and (7) additional information. We have utilized the time parameter from both traces to train the LSTM network. Time parameter contains the information of packet arrival time. Our purpose is to train the LSTM network until it learns the time interval pattern of packet arrival time from a given sequence. Data pre-processing makes the training process simple and avoid training from the divergence [11; 29]. Hence, we standardize the data with zero value of mean and unit value of the variance of the training set during the training process. Moreover, we also standardize the test set during the prediction time. Hyper-parameters in LSTM network are selected manually to make the training process more efficient. These parameters include the number of hidden units, learning rate drop factor, the maximum number of epochs, initial learning rate, and the optimizer used. The best selection of hyper-parameters  
Figure 7: Timing diagram of AI-DRX based dynamic long sleep cycle. may result in the learning with least prediction error. The training process performance can be measured in terms of RMSE. The less the value of RMSE, the better is the prediction results of the trained model on unseen data (test set). We have selected 200 and 125 hidden units in LSTM network during training process over trace 1 and trace 2, respectively. The value of the learning rate drop factor remains 0.2 for both traces. Maximum number of epochs during the training process of trace 1 are considered to be 600 while for trace 2 are 1000 epochs. Initial learning rate during the training process of both traces remains 0.004. Furthermore, the optimizer used during the training process for both traces is Adam optimizer. The above mentioned hyper-parameters reduce the RMSE during training and testing processes. We have considered the total length up to 130,820 sample values (323.886 s) of trace 1, while trace 2 has 77,470 values of time samples (417.642 s) [11]. We have divided both traces into 10% of samples as the training set and a number of different test sets randomly selected from the remaining 90% of both traces. Each test set has an equal number of samples. Figure 7(a) shows the RMSE for the initial test set of trace 1 that is as small as 12 ms. Whereas, Figure 7(b) shows the RMSE value for trace 2 is 10 ms for the first random test set. The RMSE values for random test sets from the remaining 90% samples of trace 1 and trace 2 are shown in Figure 9. It can be seen from Figure 8(a),c that the minimum value of RMSE is least at 6 ms and 5 ms on random test sets from samples 70,500 to 71,953 and 90,700 to 92,153 of trace 1, respectively. Whereas, Figure 8(b),d show the RMSE value of 6 ms and 8 ms from 10,000 to 10,899 and 65,000 to 65,899 samples of trace 2, respectively. AI-DRX can be implemented at the gNB of 5G network. During the execution of AI-DRX, we consider packet generation event, active event, dynamic long sleep event, dynamic short sleep event, ON duration event, beam searching event and feedback event. Moreover, our approach enables dynamic sleep cycles in multiple beam communications scenario for 5G networks. The packet generation event in our simulation scans the time column of both traces and produces the data packets of the identical length on the same instant in the respective trace.