# Repetition with Learning Approaches in Massive Machine Type Communications
## 1 Introduction
c. Connection density: As the demand for IoT applications increases, the density of 5G IoT devices is expected to reach approximately 1 million devices per km\({}^{2}\) in developed areas. Therefore, 5G mMTC must support a connection density value as high as 106 devices per km\({}^{2}\) while maintaining a specific quality of service. d. Latency: Although most MTC devices have considerable data transmission latency tolerance, the 5G mMTC specifications include a latency tolerance requirement to ensure satisfactory quality of service. Specifically, for each 20-byte application layer packet transmitted by a device, the latency should not exceed 10 s in a channel with an MCL of 164 dB. Both NB-IoT and eMTC are low-power wide-area network technologies in authorized spectra. NB-IoT is versatile in spectra and can support three modes of deployment. However, eMTC is faster and exhibits a broader range of applications; in a half-duplex system, the uplink and downlink speed of eMTC is 375 kbps, making eMTC applicable for IoT applications for which a medium data rate is required. Regarding peak speed, NB-IoT exhibits almost no mobility because it does not support handover between base stations, whereas eMTC exhibits more favorable mobility. Several papers have proposed the study and overview of machine learning technologies, applications, and challenges for IoT and 5G networks [21; 22]. Ref. [23] proposes the method with a game and transport theoretic approach for a fog load balancing problem. The work provides a feasible and efficient load balancing solution to ensure an optimal job assignment in the fog computing network with the NB-IoT. One study [24] used machine learning techniques that can be applied for the automation of network functions in 5G network slicing. The intelligent station recognition scheme with support vector machines (SVMs) has been proposed to achieve the fine management of stations [25]. A scheduler framework using reinforcement learning has been proposed [26]; the appropriate scheduling strategy is able to maximize user satisfaction, measured in terms of the distinct quality of the service requirements. Ref. [27] proposes the learning approach to implicitly extract channel features and recover tag symbols using the deep transfer learning (DTL) approach. In [28], a spectrum management architecture was studied and a machine learning-based spectrum decision framework for the IoT network was proposed. The eMTC requirements support two CE levels (CE Mode A and CE Mode B) [29] and several numbers of repetition, and each piece of UE can select the appropriate CE levels and number of repetitions according to the signal quality. UE with inferior signal quality requires numerous repetitions to compensate for additional signal attenuation, as depicted in Figure 1, which shows the CE level and repetitions for eMTC. In the eMTC system and mMTC application scenario, due to the main consideration of coverage and power consumption, UE pieces with poor signal quality will use more repetitions to compensate for additional signal attenuation. For the selection of the UE's CE level and repetition times, if they are too high they will waste valuable wireless resources, reduce the transmission rate, and consume more power; if they are too low, the data may not be successfully resolved at the receiving end, resulting in a higher BER (bit error rate). Therefore, we propose the learning adaptive repetition approaches based on k-nearest neighbor (KNN) and the support vector machine (SVM) for an eMTC system in an mMTC application scenario. KNN is easy to implement, has high accuracy, and is insensitive to outliers. When KNN selects appropriate training parameters, the KNN approach can achieve high discrimination accuracy. SVM can avoid the neural network structure selection and local minima problem. SVM does not have a general solution to nonlinear problems; therefore, we significantly choose the kernel function to handle it.