# A Grant-Free Random Access Process for Low-End Distribution System Using Deep Neural Network
## 1 Introduction  
The Internet of things (IoT) is shaping a world in which not only humans but also machines use the network to exchange data. In every field of life, the devices with which humans interact are becoming smarter [1, 2]. Being a smarter device means that based on the available data, the device is able to perform the tasks independently and smartly. For example, in smart grids, using data, the loads are operated in such a way that they get energy from renewable resources, and the batteries are charged such that the peak hours are avoided [3, 4]. In a smart e-health system, based on the input of the patient and available data, the device decides whether to advise the patient locally or to refer him/her to a physician [5, 6]. The autonomous car decides to take the route with minimum traffic based on the available data. In short, in every aspect of life, it is inevitable to interact with smart devices using data to make decisions, which makes life easier and hassle-free [7, 8]. Similarly the IoT finds applications in agriculture to combat plants' health issues and improving yield [9, 10]. The smart devices exchange data with other peer devices or with the servers on the network to make their decisions. The IoT devices that are using data are increasing at a rapid pace. According to [11], the expected number of connected IoT devices is approximately 13.8 billion units in 2021; however, it is projected to jump to 30.9 billion in 2025. After 2010, IoT devices have increased exponentially whereas the number of non-IoT devices has been almost constant. The comparison of IoT vs. non-IoT connections over the years is represented in Figure 1 where dark blue color represents non-IoT devices and light blue color represents IoT devices. It can be seen that the sharp rise in IoT devices is in the last decade, which incidentally is also the rise of the long-term evolution/long-term-evolution-advanced (LTE/LTE-A)standards. IoT devices require a network that can be used to transfer data between the devices or between devices and a server. Before the advent of LTE/LTE-A technology, the data services of the infrastructure networks were not adequate because of a lesser data rate. With LTE/LTE-A technology, the data pipe has also widened and the data rate has also increased [12; 13]. This allows IoT devices to use infrastructure networks such as LTE/LTE-A networks to exchange data. Prior to fourth generation (4G) networks, i.e., LTE/LTE-A, the infrastructure network (2G/3G) was human-centered such that both voice and data traffic were assumed to be human-centric. With the rise of the data rate in 4G networks, both humans and machines were considered for the traffic. With the popularity of the IoT and the devices outnumbering humans, the tendency has shifted to machine-centric traffic. This can be seen in the recommendations by International Telecommunication Union (ITU) for 5G [14; 15]. We can see that the requirements for connection density, mobility, data rate, etc., have increased and the latency has decreased. The connection density and the latency of IoT devices specifically have values of 10\({}^{6}\)/km\({}^{2}\) and 1 ms (end-to-end), respectively. Grant-free transmission in the random access process of 5G technology and beyond is a process in which the devices transmit without the handshaking process described in the next section. This grant-free transmission reduces access delay for the devices. Apart from the increase in data rate, machine/deep learning has been of massive help in smart decision-making IoT devices. As already mentioned, the devices exchange data, and based on the available data they make decisions, but with the help of techniques such as a machine or deep learning. Deep learning has been widely used in IoT applications in recent years [16; 17; 18; 19]. IoT systems require different analytic approaches compared to traditional machine learning techniques, which should be in line with the hierarchical structure of data generation and management [20].