# Repetition with Learning Approaches in Massive Machine Type Communications
## 4 Performance Evaluation
In an RLA-SVM repetition approach, to classify a new sample (the SINR set of a subcarrier), the interference function \(h_{m}(\mathrm{x})\) of each repetition and the BLER of approximately \(r_{m}(h_{m}(\mathrm{x})\) corresponding to each of the said functions must be identified. We compared the efficacy of the RLA-SVM approach with that of the RLA-KNN approach. As depicted in Figures 6 and 7, for RLA-SVM the linear kernel function is nearly as efficient as the Gaussian RBF kernel. In particular, the RBF kernel function is slightly more efficient than the linear kernel function, but the RBF kernel function involves a more complicated calculation procedure and requires a larger space for data storage. Moreover, the RLA-SVM repetition approach involves considerably lower calculation and time complexity than does the RLA-KNN repetition approach, even though the two are almost equally efficient in data transmission. Therefore, the RLA-SVM repetition approach is more applicable for an actual eMTC system than the RLA-KNN repetition approach. Figure 4: BLER versus SINR for various k values in RLA–KNN. Figure 5: Throughput versus SINR for the various k values in RLA–KNN. After analysis of the performance of RLA-KNN and RLA-SVM subject to various parameters for a single piece of UE, to optimize the eMTC system we compare the performances of RLA-KNN, RLA-SVM, and LUT. This analysis includes the probability of successful transmission, the average number of repetitions, resource utilization, and average energy consumption. The successful transmission probabilities for RLA-KNN, RLA-SVM, and the LUT are shown in Figure 8. The probability of successful transmission gradually decreases as the intensity of the number of pieces of UE per sector increases (i.e., as more users attempt access). Failure probability is observed for high intensity with the use of the LUT method. Apart from collision, the increased failure rate is due to the interference and radio channel effects, caused by the presence of many pieces of UE with wide coverage close to the eNB, which affects such UE pieces farther away in the same coverage zone. The probabilities of successful transmission for RLA-KNN and RLA-SVM are higher than for the LUT method because the repetition number selection policy is considered to be an interference factor from the training data. Figure 6: BLER versus SINR for RLA–SVM–RBF, RLA–SVM–linear, and RLA–KNN. Figure 7: Throughput versus SINR for RLA–SVM–RBF, RLA–SVM–linear, and RLA–KNN. Figure 9 presents the average numbers of repetitions in the RLA-KNN, RLA-SVM, and LUT methods. With the RLA-KNN and RLA-SVM approaches, a significant reduction in repetition is observed. The repetition for the LUT method is noted to be greater than for the RLA-KNN and RLA-SVM methods. In many cases, the LUT method uses excessive numbers of repetitions to achieve successful transmission. In RLA-KNN and RLA-SVM, the number of repetitions required to achieve successful transmission is significantly reduced by the training data and learning processes. The resource utilization for RLA-KNN, RLA-SVM, and the LUT is presented in Figure 10. The resource utilization for the three methods is seen to decrease as the number of UE pieces per sector increases. RLA-KNN and RLA-SVM also exhibit higher resource utilization than the LUT because more resources are utilized in the same time period if a shorter repetition is adopted. The result indicates that a smaller number of repetitions is associated with greater resource utilization. Figure 8: Probability of successful transmission according to UE numbers. Figure 9: Average number of repetitions. Figure 11 presents the average energy consumption per sector in the transmission mode by all users. This decreases the number of repetitions and, thus, power consumption. With the suitable reduction in repetition using the RLA-KNN and RLA-SVM methods, energy consumption is also significantly reduced. A significant reduction in energy consumption is achieved for the uplink compared with for the LUT method.