# Repetition with Learning Approaches in Massive Machine Type Communications
## 1 Introduction  
Machine type communication (MTC) describes data exchange and communications between machines and is a crucial component in areas such as smart cities, traffic optimization, smart poles, e-medicine, and the industrial Internet of Things (IoT) [1, 2]. Following the increase in MTC applications, the number of connected wireless devices has risen dramatically; the number of wireless MTC devices is expected to reach several billion [3]. However, this has created a critical challenge for the devices' network-access capability. Massive MTC (mMTC) is the primary third-generation partnership project (3 GPP) fifth-generation (5G) application scenario and technology implemented in IoT devices [4]. The respective technology involves connecting a massive number of components. For each square kilometer of area in a developed region, the machine-machine communications between 1 million devices are expected. Such communications are primarily for small quantities of data with relatively high latency tolerance. The components must be cost-efficient and equipped with long battery lives. 3 GPP enhanced MTC (eMTC) and 3 GPP narrowband IoT (NB-IoT) are 5G IoT technology standards that address the requirements of 3 GPP 5G mMTC application scenarios. Studies have explored the physical characteristics and design goals of eMTC and NB-IoT [5, 6, 7]. In the long-term evolution (LTE), the 5G, advanced eMTC, and NB-IoT user equipment (UE) switches from idle mode to connected mode through random access (RA) [8, 9]; this process is achieved through a four-way handshake, which involves apreamble transmission, an RA response, a connection request, and a connection resolution. When the UE transmits data in a highly synchronized manner, the signaling capacity of the evolved node B can be easily exceeded, causing severe system congestion [10]. [PERSON] and [PERSON] [11] proposed an algorithm involving beehive search and initial synchronization. The simulation results confirmed that the algorithm provides satisfactory network efficacy even for devices with an extremely low signal-to-noise ratio. In [12], a maximum-likelihood detector was applied for initial timed collection in IoT devices. The average detection latency of the detector was half as high as that of the related method, and the energy required for each timed collection decreased by 34%. An approach integrating agent-based modeling and simulation was employed for IoT system efficacy analysis in [13; 14]. The agent-based cooperative smart object [15] approach uses OMNeT++ for simulation. [PERSON] et al. [16; 17] proposed two novel resource coordination methods that involve bridging dynamic sensing tasks with heterogeneous IoT sensors and controlling the operating cycles of devices to save energy. In [18], eMTC infrastructure coverage efficacy was analyzed and compared with that of LTE technology to reinforce the eMTC coverage at 15 dB. The design of mMTC technology must fulfill the following four requirements:  
a. Coverage: The coverage of mMTC must attain a maximum coupling loss (MCL) of 164 dB [19]. Even when the signal from the transmitter to the receiver is attenuated by as much as 164 dB, the receiver must successfully decipher the packet. In addition, because increasing coverage through repeat transmission reduces the data transmission rate substantially, 5G mMTC coverage must also be successfully implemented at a transmission rate as high as 160 bit/s. Therefore, selecting an appropriate coverage enhancement (CE) level and number of repetitions for the signal quality is critical. b. UE battery life: Devices with long battery lives are required in 5G MTC applications such as smart electric and water meters. These devices may be installed in environments that impede battery replacement or in which the cost of a battery replacement is excessive. Therefore, mMTC devices must have battery lives of no less than 10 years [20], similar to those of eMTC or NB-IoT devices.