# Repetition with Learning Approaches in Massive Machine Type Communications
## 4 Performance Evaluation  
The RLA was applied to evaluate efficacy. The performances for RLA-KNN and RLA-SVM were analyzed subject to various parameters for a single piece of UE, evaluatedin a simulated environment and compared with those of the common lookup tables (LUTs) from the eMTC system optimization. This analysis included the successful transmission probabilities of the UE, the average numbers of the repetitions, the resource utilization, and the average energy consumption. The traffic model defined in 3 GPP TR 36.763 [36] was used as the traffic model in the simulation, where the number of eMTC devices was set to 20,000-200,000 per sector; the average number of uplink traffic reports was set to 20/s; and the payload size of each device ranged from 20 to 200 bytes. In the simulation, each point represented the average of 10,000 samples, each of which was acquired between the first uplink transmission and the end of the last uplink transmission (i.e., the observation interval). The relevant simulation environment parameters are illustrated in Table 1. The indicators used to evaluate the performance of the proposed approach were as follows: (1) BLER versus signal to interference plus noise ratio (SINR) for various k values in RLA-KNN using a single piece of UE; (2) throughput versus SINR for the various k values in RLA-KNN using a single piece of UE; (3) BLER versus SINR with the RLA-SVM-radial basis function (RBF), RLA-SVM-linear, and RLA-KNN for a single piece of UE; (4) throughput versus SINR with RLA-SVM-RBF, RLA-SVM-linear, and RLA-KNN for a single piece of UE; (5) the successful transmission probabilities of the UE pieces; (6) the average number of repetitions; (7) the resource utilization; and (8) the average energy consumption. The RLA-KNN repetition approach consists of a training phase and a test phase. The training phase requires a training set, each of which corresponds to a unique SINR sequence and is assigned to a repetition number. During the training phase, all the vectors in the training set must undergo all repetitions for accurate classification of the set to the repetition number. The vectors are then classified according to the BLER value of each repetition. As shown in Figures 4 and 5, a higher \(k\) leads to higher system efficacy. However, after \(k\) exceeds a certain value, the system efficacy begins to drop. Ordinarily, a higher \(k\) limits the effect of erroneous classification on the training results more effectively, yielding a smaller classification error during the test phase and higher system efficacy. However, because of a limit in the size of the training set, the error rate starts to increase after \(k\) exceeds a certain value, lowering system efficacy. As shown in the simulation results, the system efficacy was maximized when \(k\) was 30; so, this value was employed in the follow-up simulation. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Assumption** \\ \hline Sector per cell & 3 \\ Fractional frequency reuse & Hard \\ Allocation size & 12 tones @ 15 KHz \\ Resource Unit (RU) & 1 ms \\ eMTC Bandwidth & 6 PRBs (1.08 MHz) \\ Channel Model & Typical Urban (TU) \\ Payload size & 20–200 \\ Number of eMTC UE pieces per sector & 20,000–200,000 \\ DL Antenna Configuration & gNB: 2 Tx/ 4 Tx, UE: 1 Rx \\ Uplink Antenna Configuration & gNB: 2 Rx/ 4 Rx, UE: 1 Tx \\ Doppler Spread & 1 Hz \\ gNB Tx power & 43 dBm \\ UE Tx power & 20 dBm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation Parameters. Accordingly, when the number of data in the system is high, the KNN approach requires a complicated calculation procedure and cannot be unsuitably applied in actual network communications without dimensionality reduction. Therefore, the SVM approach is relatively flexible because it can employ numerous kernel functions, reducing the complexity of the calculation procedure considerably.